RETRACTED

This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:

(1)Discrepancies in scope(2)Discrepancies in the description of the research reported(3)Discrepancies between the availability of data and the research described(4)Inappropriate citations(5)Incoherent, meaningless and/or irrelevant content included in the article(6)Peer-review manipulation

The presence of these indicators undermines our confidence in the integrity of the article’s content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process.

Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity.

We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation.

The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

Mathematical Problems in Engineering
Volume 2021 (2021), Article ID 6652752, 11 pages
https://doi.org/10.1155/2021/6652752

Research Article

A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

Yonghua Zhou,¹ Ting Huang,² and Shangbin Li³

¹Department of Physical Education, Tianjin Foreign Studies University, Tianjin 300204, China
²School of Physical Education and Educational Science, Tianjin University of Sport, Tianjin 301617, China
³Physical Education Department, Harbin Engineering University, Harbin 150001, China

Correspondence should be addressed to Shangbin Li; sports@hrbeu.edu.cn

Received 15 December 2020; Revised 27 February 2021; Accepted 26 March 2021; Published 7 April 2021

Academic Editor: Sang-Bing Tsai

Copyright © 2021 Yonghua Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The paper studied the problems of soccer detection and tracking in soccer tracking, in soccer detection; as the size of the soccer is too small to extract distinguishable feature, it is difficult to detect the soccer automatically. To solve this problem, a soccer detection algorithm was based on class weighted spatial Fuzzy C-means (ws-FCM) was proposed. Firstly, the target function of the spatial Fuzzy C-means was improved. Subsequently, a bi-threshold strategy was proposed to detect the soccer automatically. In the aspect of soccer tracking, existing methods fail to detect the soccer when it was occluded by several players successively. To solve this problem, the motion state of soccer of broadcast soccer video was analyzed, which is inspired by the contextual cueing effect of human visual search. According to the motion state of the soccer, parameters updating function of dynamic Kalman filter (DKF) were improved. Thus, a soccer tracking algorithm based on multiple search regions dynamic Kalman filter (MDKF) was proposed, which enhances the robustness of soccer tracking by extending the search area. The experiments show that the proposed algorithm can automatically detect soccer in images with high detection accuracy and can track the soccer more robustly, with better occlusion handle ability.

1. Introduction

The research goal of video content analysis technology is to establish the mapping between low-level features and high-level semantics, so as to automatically acquire semantic content and build user-oriented application systems to provide users with more convenient content acquisition services. Video content is diverse. Therefore, it is difficult for video content analysis technology to be universal, and the design of corresponding identification methods needs to be in accordance with the characteristics of the analyzed video content. The research object of video content analysis is usually a specific type of video with urgent content analysis need, such as soccer videos. Soccer videos have a broad audience and significant business value, and their content analysis technology has attracted many researchers [1, 2]. The demand for the content analysis of soccer videos comes not only from the requirements of ordinary users for selective viewing of wonderful clips, specific events, etc., but also from soccer fans and soccer professionals (coaches, players) to quickly locate the needs of certain types of offenses from the many offenses. These users expect to analyze the game strategy and tactics by the previous game videos to achieve a deeper understanding of the game process and improve the performance of the game [3]. Research on soccer video game strategy and tactics analysis is relatively scarce, and related requirements have not been fully met. Therefore, this paper focuses on this need.

Humans have flexible and powerful video content understanding capabilities and can accurately and automatically identify specific content in videos [4]. The essential information contained in the video is the human cognitive content. On a wide recognition of this point of view, human cognitive principles become an essential reference for video content analysis researchers [5]. Soccer video content analysis technology focuses on automatically analysis of related content based on the content characteristics and analysis needs of soccer videos. Therefore, research on content analysis methods with utilization of human cognitive principles and the content characteristics of soccer videos will likely expand the research ideas of soccer video tend to enrich related research ideas.

Soccer is the object which is scrambled for and controlled by both teams in the competition. Its motion characteristics reflect team attack strategy, playing a supporting role in analyzing the high-level semantic contents [6, 7]. In the broadcast soccer videos, the soccer size is too narrow to be directly detected. Although the shape exclusion method relieves the difficulty, it is of bad practicability because it is required to obtain the binary image of foreground objects [8–10]. Besides, in the motion course, soccer tends to merge and occlude with marking lines or players, leading to missing of soccer measuring values and failure of tracking. In order to enhance the robustness of tracking and maintaining processes, Kim et al. proposed a dynamic Kalman filter algorithm that merges the occlusion processing mechanism. However, the method also loses rubber soccer with complicated occlusion [11–13].

Soccer tracking is a typical human visual motion tracking process. Therefore, we can classify soccer tracking into two phases, soccer tracking and tracking maintenance, which are each corresponding to target acquisition and motion tracking of human visual tracking [14]. At the stage of target acquisition, the visual system aims to enter into the visual system to select visual stimulation and determine tracking objects. The preference of such selection is associated with the scene priori of pending tracking object, with from-top-to-bottom factor. Hence, in soccer detection, it is possible to optimize the detection method according to the scene feature of soccer video to increase the automation degree of soccer detection method. In the motion tracking stage [15], in order to overcome the influence of occlusion on tracking maintenance, the human visual search system is able to complete visual tasks like search and tracking in the aid of contextual cueing effect. Concerning principle of the soccer tracking method, it is likely to intensify the robustness of the tracking algorithm.

In sports video analysis, the trajectory obtained by object tracking can be used for the analysis of much high-level semantic content. Therefore, tracking important objects has always been an important aspect of sports video analysis research. Soccer is one of the most critical objects in soccer videos. Soccer position and trajectory information can be widely used in a video summary, the region of interest (ROI) coding, tactical analysis, etc. Therefore, detecting soccer and tracking soccer is a valuable research content of soccer video content analysis. Modern soccer game uses a truncated icosahedron sewn with leather, which is unique during the game. Therefore, soccer detection and soccer tracking in soccer videos are complementary. In videos, soccer often appears as a circle. Based on this feature, Orazio et al. [16] first attempted to detect soccer in images using circularity features. Subsequently, Tong et al. [17] proposed a candidate soccer detection method that combined circularity and color. In order to express the needs of the game, the camera needs to cover a certain area of the field. Therefore, the area occupied by soccer in the broadcast video is relatively small, and the distinguishability of the extracted features is not high. Many objects, such as over-divided indicator lines and players, have similarities in color and shape for soccer. Therefore, the above methods have limited improvement in soccer detection performance and cannot accurately detect soccer areas.

Because of the difficulty in directly extracting soccer features, Yu et al. [6] proposed a soccer detection method based on the exclusion method. This method first obtains a binary image of the foreground object, then excludes most of the areas that do not belong to the soccer through a set of shape rules, and finally verifies the remaining candidate soccer. Despite some shortcomings in obtaining foreground objects, the exclusive method is still a practical method for soccer detection and tracking. Based on this method, Liang, Choi et al. [8] proposed a multiframe based soccer detection. The soccer area detected by the exclusion method is not unique. In order to determine the position of the soccer, the researchers further proposed a soccer tracking method based on the Kalman filter and the trajectory optimization method based on the exclusion method. The above methods all have a certain dependence on the soccer detection effect based on the exclusive method [10]. However, the exclusion method can only detect unblocked soccer. When the soccer is occluded, the exclusive method cannot detect the soccer’s position. In this case, the tracking performance of the above method is also affected.

In broadcast soccer videos, soccer is often blocked by players. Therefore, it is necessary to study the soccer tracking method in the case of occlusion. It is generally believed that in order to maintain tracking of objects under occlusion conditions, corresponding occlusion processing mechanisms need to be integrated into the tracker [18]. Based on this principle, Seo et al. proposed a soccer tracking method with the occlusion processing mechanism; this method marks the player closest to soccer as a “has ball.” If soccer disappears during tracking, the tracker will search for soccer near the “has ball.” Choi et al. expanded the search of soccer to include players close to the player with the ball. These two methods can keep the soccer tracked to a certain extent when the players block the soccer. However, when soccer is blocked by something other than the player, the above method will still have tracking errors. In order to further enhance the tracking ability under occlusion, Kim et al. [13] proposed a DKF tracking method. Typical Kalman filter (TKF) algorithm only uses the measured position of soccer for parameter update. Therefore, when soccer is blocked and cannot be measured, TKF is not be able to make the correct parameter updates and may cause the soccer to be lost. To this end, DKF dynamically adjusts TKF’s parameter update strategy based on the results of object detection in the search area. Due to this dynamic mechanism, DKF has a certain occlusion processing capability, and the soccer tracking results are more robust. However, DKF’s parameter update functions are still not perfect. When multiple players continuously block the soccer, DKF may still lose track of the soccer.

Based on the above analysis, this paper discusses the soccer tracking problem from two aspects: soccer detection and tracking maintenance. In soccer detection, with regards to the lack of automatic detection method of soccer, the paper presents an automatic soccer detection method based on class weighting sFCM, in accordance with features like changeable facade patterns of soccer and susceptibility to interference. The method increases the error weight of the foreground object by means of target function optimization and deduces soccer leak detection caused by sFCM. On that basis, the paper develops a detection method based on dual threshold strategy, realizing automatic soccer detection. In soccer tracking, by learning from the process of human visual search context prompt effect, this paper analyzes the motion state of soccer, optimizes the parameter updating function of dynamic Kalman filter according to the motion state of soccer, and proposes a multiarea search dynamic Kalman algorithm. The filtering method improves the robustness of the soccer tracking method.

2. Soccer Detection Based on Class-Weighted SFCM

On the ground that appears in broadcast videos, there are lots of objects which have similar colors with the soccer. Thus, it is difficult to detect soccer by means of color features. Compared with other objects, soccer occupies too smaller area and the area is usually elliptical or round, which differs apparently from other objects by shape. So, the soccer detection method based on shape is widely applied. The premise to detect rubber soccer with the use of shape difference is to binarize images which are waiting for analysis. At present, there is no effective binarizing method oriented to the soccer detection. To address the question, the authors developed a binarization method based on class weighted sFCM on the foundation of the soccer detection scene priori.

Furthermore, on that basis, the soccer detection approach based on dual threshold strategy is designed. Therefore, we used local difference image to binarize and detect soccer detection. The clustering method is a typical image binarizing method. FCM is a popular one of various fuzzy clustering algorithms and widely applied in image segmentation. However, the FCM method cannot describe spatial features of the image and obtained binary images mostly have a noisy area. Therefore, Chuang et al. got sFCM by improving the membership function and decreased noises that are easily found in segmentation results by FCM. However, when completing binarization oriented to the soccer, sFCM easily cause leak detection of soccer area. Hence, first of all, we probe into the principle of FCM and sFCM and then optimize error objective function of sFCM according to the requirement of soccer detection [19].

In the sense of clustering principle, the basic idea of FCM is divided n data into k groups and makes objective function value at the minimal. During the clustering, the objective function defined by Bezdek is often adopted. It is shown in the following equation:

Under the constraint condition , the necessary condition for the equation (1) to obtain the extreme value is derived by the Lagrange multiplier method. It is shown as follows:

In order to get the best partition of data set, the FCM algorithm iteratively updates the and , which minimizes the objective equation (1), and the main steps are

(1)The number of clusters j is determined, membership index m, stop threshold (2)The initial cluster center from is randomly selected(3)The following operations are repeated, until

According to the above steps, the use of FCM for image two values only needs to set the number of categories. Then, the membership value of each pixel is divided according to the following equation:

Compared with the general data, one of the characteristics of the image data is the high correlation of neighboring pixels. The gray values of the domain pixels are usually similar. Therefore, adjacent pixels are more likely to belong to the same category. Making good use of this relationship can effectively reduce the false detection of clustering results. However, in the classical FCM, the correlation between neighboring pixels is not modelled. Thus, there is much noise in the two value image obtained by FCM. In order to ensure that better use of the neighborhood correlation in the image to eliminate the detection noise, Chuang et al. proposed the FCM algorithm to fuse the spatial information-Spatial Fuzzy C-Means (sFCM). The basic idea of this method is to add spatial information into the calculation of membership degree. Based on equation (3), the spatial information function is shown as follows:

With regards to the questions mentioned above, the authors proposed class weighted fuzzy C mean clustering algorithm (wsFCM) in line with the specific requirement of soccer detection binarization. The primary thought of the algorithm is to add different weighting factors in accordance with different quantities of class pixels, so as to equilibrize the impact of class quantity difference in the clustering result. In order not to lose generality, we define wsFCM objective function as follows:

According to definition of objective function , we learn that should be inversely proportional to the quantity of sample. So, as long as the quantity of foreground object pixel and the contextual pixel is determined, can be determined. The triangle threshold method can binarize pixels of a majority of foreground objects. Some pixels are falsely detected, but there are very fewer. Therefore, we utilize the triangle threshold method to decide the value of , solving in the way as follows:

Summing up the above process, the iterative process of wsFCM algorithm mainly includes the following steps:

(1)The number of clusters k is determined, membership index m, stop threshold , category weight (2)The initial cluster center from is randomly selected(3)The following operations are repeated, until

Figure 1 is an example of foreground object pixel detection results.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 1: Binarization examples of foreground object. (a) Video frame, (b) SFCM, (c) WsFCM, (d) Otsu, (e) Rosin, (f) Canny edge detector.

With the automatic threshold, plenty of falsely detected pixels exist in the foreground object detection result by the Canny operator, impossible to detect the soccer effectively. Comparatively, the Otsu and triangle threshold method (Rosin method) got a fewer number of noisy areas in the binarized images. The sFCM is advantageous in eliminating binarized image noise, but soccer is lost in some frames. As shown in Figure 1(b), the proposed wsFCM can achieve better noise removing the effect in the meantime of not losing soccer.

Despite wsFCM can effectively suppress noise in the binarized images, oversegmented marking lines appear in the binary results. Due to the movement of soccer and camera, the gray value of soccer and other objects will vary. Thus, we use a triangle threshold method to remove oversegmented areas.

The gray values of the playground’s pixels are of identical size; hence, those pixels’ values are smaller in local difference images, mostly distributed close to zero value. Background pixels of smaller gray values are dominant in local difference images and form a central peak in the histogram. It is more appropriate to employ to binarize the images which have a single peak histogram. Figure 2 is schematic of the triangle threshold.

(a)

(b)

Figure 2: Schematic of the triangle threshold. (a) Histogram. (b) Triangle threshold.

After input image is converted to local difference image, the method firstly gets the binary image and of foreground pixel area by the triangle threshold method and wsFCM method; next, from and , we acquire candidate soccer area through a set of predefined shape rules; finally, the results were combined by two methods. Since the width, height, and area of soccer region and the aspect ratio are obviously different from other regions. We detected candidate soccer region by following rules. It is shown in

The resolution of the video in this paper is . At this resolution, the typical values of the parameters in the equation (4) are , , , and . The candidate soccer area detected using this method is shown in Figure 3. The analysis shows that the method can effectively eliminate the over segmentation in , thus improving the accuracy of soccer detection. It is shown in Table 1.

(a)

(b)

(c)

Figure 3: Example of soccer detection results. (a) Triangle threshold, (b) WsFCM, (c) proposed method.

Table 1: Soccer detection results of the bi-threshold method.

Video number
Contains soccer frames
Error detection
Miss
Accuracy (%)
Recall (%)

#1
102
3
1
97.12
99.02

#2
89
3
4
96.59
95.51

#3
61
0
0
100.00
100.00

#4
74
2
1
97.33
98.65

#5
40
1
1
97.50
97.50

#6
118
2
0
98.33
100.00

#total
484
11
7
97.75
98.55

A comparison between Figures 1 and 3 also shows that there is a mistake in the area near the goal. The position is the soccer match referee penalty kick should be placed in the position of soccer, that is, the penalty point (penalty mark, also known as the penalty point, 12 yards). In broadcast soccer, spot color, size, shape, and soccer are very similar; it is hard to distinguish with the soccer detection methods; it needs to be distinguished by penalty shot detection methods.

3. Soccer Tracking Method Based on Multiarea Search Dynamic Kalman Filter

From the theoretical perspective, soccer tracking can be modelled to Bayesian state estimation problem. State refers to various motion characteristics of objects to our concern. According to the opinions of Bayesian estimation, the essence of object tracking is to determine recursively the confidence level of state vector at time t according to the acquired observational data , that is, posterior probability density function of estimation state vector. The Bayesian filter utilizes two models regarding the object state to make inference through a state transition equation in

Particle filter and Kalman filter are currently the popular Bayesian filtering methods. Since the soccer area is smaller in broadcast videos, it is rather hard to fetch discrimination features, hardly representing the probability density function of state in the manner of sampling. Therefore, Kalman filter is extensively applied for soccer tracking. Kalman filter was raised by Kalman in 1960 [20] so as to solve the linear filtering problem of discrete data. In the framework of the Bayesian filter, Kalman filter takes advantage of a prediction-feedback mechanism to obtain the state of system. In other words, it predicts the state at one time, and then it measures system state and uses as feedback as to rectify the predicted state. To be more specific, the Kalman filter includes primarily two parts: time updating equation and measure updating equation. At one specific moment, Kalman filter predicts the current state of system as per time updating equation; next, it gets measuring value and uses it to update equation as to modify the predictive value of the system.

3.1. Analysis of Soccer Motions in Broadcast Soccer Videos

Soccer motion state analysis aims to find out the motion pattern of soccer when it can be detected. Based on the moving course of soccer, we concluded that the soccer gets lost, because it merges together with mark lines or it is occluded by players. So, in those cases, we analyze soccer movement. When soccer merges with marking lines on the pitch, the soccer is freewheeling, and its motion state basically will not change. Then, we predict the position of soccer by its original motion state. When the soccer is sheltered by the player, soccer may be freewheeling or may be under the control of player. If the soccer is not physically contacted with player, but, similar to the above situation, that is the soccer is freewheeling. At this moment, soccer motion state does not change but merely passing by player area. If the soccer is physically contact with player, it is under the manipulation of players, and that the soccer will move together with player. Meanwhile, due to tackle by players, there would be the situation when soccer is sequentially occluded by players. In other words, players who occlude the soccer would change from time to time. It is noted that no matter when the occlusion happens during the soccer coasting or being under the control of the player, the position of soccer after being occluded would be near player. When the direction of soccer’s motion direction changes, the soccer gets out of the player’s control and appear near the dominant player. Based on the above analysis, it can be concluded that the state of soccer can be classified no occlusion, indication line fusion, single player occlusion, and multiplayer occlusion.

3.2. Basic Principle of Multiarea Search Dynamic Kalman Filtering Method

Motion state of soccer is foundation of intensifying the robustness of soccer tracking method. According to the analysis of soccer motion state, the paper extends parameter updating function of dynamic Kalman filter (DKF) and proposes a multiarea search dynamic Kalman filtering algorithm (MDKF) which is more adaptive to soccer moving features. The method aims to optimize parameter updating function of DKF in accordance with motion state of soccer and introduce multiarea search mechanism into parameter updating procedure as to boost the robustness of soccer tracking process. The similarities to DKF method, MDKF includes a three parts: time updating, measure updating, and dynamic adjustment of parameters.

In terms of time update, MDKF is used to predict the state of the system at the next time by the following equation:

The state of the equation (11) is the position and motion information of the soccer. Therefore, the system is set according to the following equation:

The motion process of soccer may refer to these four states: no occlusion, merging with marking lines, single occlusion, and multiple occlusions. Therefore, parameter updating ways in MDKF should consist of measuring mode (MM), prediction mode (PM), single occlusion mode (SOM), and multiple occlusion mode (MOM). In MOM, although player who occludes the soccer has changed, soccer still appears close to other players. So we can predict any location where the soccer possibly appears by search area expanding.

The search region extension mechanism used in this paper is shown in Figure 4, which includes the following four steps:

(1)The distance from the nearest player to is recorded(2)Side to as the center of the square area of T, denoted by , is selected(3)All players in the area as are selected(4)The near the side length of 20 + DLPA/2 region as new search area

Figure 4: An illustration of the candidate soccer search area extending procedure.

4. Experimental Analysis and Results

The key to the soccer tracking method is to maintain tracking of soccer after being occluded. As mentioned before, in the moving course, soccer may be occluded by different objects such as marking lines, single player, and multiple players. Hence, we choose several competition video clips that include different occlusions to evaluate the tracking performance of the proposed tracking approach. They are extracted from SoccerNet (https://soccer-net.org/), including games between South Africa and Mexico, Japan and Cameron, Spain and Switzerland, Slovenia and United States, and Brazil and Cote d’Ivoire, totaling over 1000 frames. For the convenience of comparison, we make size of those videos to . Merging with marking lines and single occlusion are often seen in soccer videos. So, matched videos used in the test contain merging with marking lines and single occlusion. Besides, multiple occlusions are often seen in the match. So, testing video clips #1 and #2 and #4 and #6 both include single occlusion and multiple occlusions.

Kim et al. raised a soccer tracking method based on DKF and achieved more robust tracking result. Thus, we use the method to compare tracking results. First of all, the visual contrast between the two methods is compared by using video clips #1 and #2. Then, the soccer tracking results were compared based on calculation of Euclidean distance between the tracked soccer position and the real soccer position (manual annotation).

4.1. Visual Comparison of Tracking Results

In order to appraise vividly the performance of tracking robustness of the soccer tracking method proposed in the paper, we take the first two segments of testing videos to make visual comparison of soccer tracking results. The two video segments contain no occlusion, merging with marking lines, single occlusion, and multiple occlusions. The first row is tracking result by the DKF method; the second row is tracking result by the MDKF method. In the tracking sample picture, yellow and black blocks stand, respectively, for soccer location tracked by algorithms and that by manual annotation. It is shown in Figures 5 and 6.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Figure 5: Comparison on the tracking results of the video clip #1. (a) frame 002, (b) frame 021, (c) frame 035, (d) frame 177, (e) frame 188, (f) frame 225, (g) frame 227, (h) frame 256, (i) frame 287.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Figure 6: Comparison on the tracking results of the video clip #2. (a) frame 002, (b) frame 019, (c) frame 020, (d) frame 049, (e) frame 126, (f) frame 163, (g) frame 165, (h) frame 182, (i) frame 200.

The tracking result with testing video sequence #1 is shown in Figure 5. Figure 5 shows the racking result with testing video sequence #1. In that sequence, soccer moves from the midfield to the front of goal. In the moving course, soccer’s motion state changes from merging with marking lines, single occlusion to multiple occlusions. From tracking result in frame 002–035 in Figure 5, we find when the soccer is overlapping with marking lines in the field or sheltered by single player, DKF and the proposed MDKF method can maintain tracking of the soccer. However, when the soccer is occluded by several players, just as tracking results in frame 177–287 in Figure 5, DKF lost the soccer being trailed. However, the proposed MDKF method can redetect the soccer and maintains tracking of it.

The visual tracking result of testing sequence #2 is shown in Figure 6. In that sequence, soccer’s motion direction is opposite to the testing sequence #1. Soccer is moved from the position near the goal to the central area of the playing pitch. Soccer that is being sheltered by several players appears in the center of the playground, as observed from the tracking result in the frame 126–165 in Figure 6. After being occluded by a couple of players, soccer gets rid but it is detected again by the proposed MDKF method, which realizes continuous tracking of soccer movement. Since search area is too small to detect soccer again, the parameter updating function of the DKF method is not able to do correct parameter updating and that it loses tracking of the soccer. In the later part of the video, DKF recovers tracking of the soccer, because due to camera movement, soccer returns to the detection window of DKF and it continues tracking the soccer. In short, the DKF method can lose tracking of soccer after it is occluded. The reason is that the parameter updating function gets wrong as a result of the failure of soccer detection. Through in-depth analysis and according to the characteristics of soccer’s motion mode, the paper optimized parameter updating function of DKF and thus increased the robustness of soccer tracking process.

4.2. Quantitative Comparison of  Tracking Results

In order to compare the differences between the DKF and the MDKF methods proposed in this paper in the tracking results, the tracking results of the two methods are compared in this paper. In this paper, firstly, the real position of the soccer is manually marked on the video image and then the Euclidean distance between the position of the tracked soccer and its position is used as the quantitative evaluation index.

In order to compare the tracking results on the whole sequence, this paper calculates the mean of the Euclidean distance between the two methods in the video sequence and the position of the soccer position; it is shown in Table 2.

Table 2: The mean Euclidean distance between tracked position and labelled position.

Video sequence
#1
#2
#3
#4
#5
#6
Mean value

DKF
130.5
32.56
4.75
56.78
164.77.
135.76
87.56

MDKF
7.51
8.90
4.75
12.45
7.89
23.67
11.14

According to Table 2, in the test video #1, #2, #4, and #6, there is a big difference between DKF tracking results and MDKF tracking results. The average distance between the position and the position of the DKF tracked in the test video is much larger than the average distance between the position and the position of the MDKF. This phenomenon is closely related to the existence of multiplayer occlusion in the test video.

When the multiplayer occlusion occurs, DKF will lose track of the soccer, while the MDKF can continue to maintain the tracking of soccer. After the failure of tracking, the distance between the tracking position of the DKF and the real soccer position will not increase. While the MDKF can continue to maintain the tracking of soccer, its tracking position and the distance between the real soccer position to maintain a smaller range.

At the same time, the DKF and MDKF methods in the test video on #3 get consistent tracking results. The reason is that a single player causes the occlusion in #3. In this case, both DKF and MDKF can be used to track the position of soccer. Therefore, the two methods get the same result. The tracking of the target causes the difference between the tracking result and the location.

In the same distance threshold, MDKF can get higher tracking accuracy. The reason is that MDKF enhances the robustness of the soccer tracking process, thus effectively reducing the distance between the tracking position and the real position of the soccer. It is shown in Figure 7.

Figure 7: Tracking accuracy of the DKF and MDKF with different detection thresholds.

5. Conclusions

In this paper, we study the soccer tracking problem from two aspects: soccer detection and tracking maintenance. In the aspect of soccer detection, the problem of the lack of automatic soccer detection method exists. In this paper, a class of weighted sFCM-based automatic soccer detection method is proposed. The method is based on the characteristics of the number of foreground objects such as soccer, which is less than the number of background pixels. WsFCM algorithm is proposed for soccer detection, by increasing the weight of the foreground object category error to reduce the missed soccer. In this paper, a soccer tracking method based on a multiregion search DKFr is proposed. In this method, the motion state of soccer is inspired by the human visual search. According to the motion state of soccer, the parameter updating function of dynamic kalman filter is optimized. The robustness of the soccer tracking method is improved by searching the regional expansion.

Data Availability

No data were used to support this study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

P. Nooralishahi, C. K. Loo, and L. W. Shiung, “Robust remote heart rate estimation from multiple asynchronous noisy channels using autoregressive model with Kalman filter,” Biomedical Signal Processing and Control, vol. 47, pp. 366–379, 2019.

M. Kaushal, B. S. Khehra, and A. Sharma, “Soft computing based object detection and tracking approaches: state-of-the-art survey,” Applied Soft Computing, vol. 70, pp. 423–464, 2018.

A. Nadzilah, D. M. Gandana, J. Muliadi, and Y. Daryanto, “Application of Kalman filter to track ship maneuver,” in Proceedings of the 2017 5th International Conference on Cyber and IT Service Management (CITSM), pp. 45–50, Denpasar, Indonesia, August 2017.

P. R. Kamble, A. G. Keskar, and K. M. Bhurchandi, “Ball tracking in sports: a survey,” Artificial Intelligence Review, vol. 52, no. 3, pp. 1655–1705, 2019.

R. Ben-Ari and O. Ben-Shahar, “A computationally efficient tracker with direct appearance-kinematic measure and adaptive Kalman filter,” Journal of Real-Time Image Processing, vol. 11, no. 2, pp. 271–285, 2018.

X. Yu, Q. Tian, and K. W. Wan, “A novel ball detection framework for real soccer video,” in Proceedings of the International Conference on Multimedia and Expo, pp. 265–281, IEEE, Baltimore, MD, USA, July 2003.

X. Yu, H. W. Long, and C. Xu, “Trajectory-based ball detection and tracking in broadcast soccer video,” IEEE Transactions on Multimedia, vol. 8, no. 6, pp. 1164–1178, 2016.

D. Liang, Y. Liu, and Q. Huang, “A scheme for ball detection and tracking in broadcast soccer video,” in Proceedings of the Pacific Rim Conference on Multimedia, Springer, Jeju Island, Korea, November 2015.

H. Ramesh, S. Arockia Edwin, and S. Barath Kumar, “Multiple model filter based position tracking in CNC machines,” in Proceedings of the 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), pp. 1–8, Chennai, India, March 2017.

V. Pallavi, J. Mukherjee, A. K. Majumdar, and S. Sural, “Ball detection from broadcast soccer videos using static and dynamic features,” Journal of Visual Communication and Image Representation, vol. 19, no. 7, pp. 426–436, 2008.

X. Tong, T. Wang, W. Li, and Y. Zhang, “A novel algorithm for effective ball tracking,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 24, no. 3, pp. 359–379, 2010.

J.-Y. Kim, C.-H. Yi, and T. Y. Kim, “ROI-centered compression by adaptive quantization for sports video,” IEEE Transactions on Consumer Electronics, vol. 56, no. 2, pp. 951–956, 2010.

J. Y. Kim and T. Y. Kim, “Soccer ball tracking using dynamic Kalman filter with velocity control,” in Proceedings of the 6th International Conference on Computer Graphics, Imaging and Visualization, IEEE, Tianjin, China, August 2009.

M. Elzoghby, F. Li, U. Arif, and I. Arafa, “Small UAV localization based strong tracking filters augmented with interacting multiple model,” in Proceedings of the 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, January 2018.

G. Ligorio and A. M. Sabatini, “A novel Kalman filter for human motion tracking with an inertial-based dynamic inclinometer,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 8, pp. 2033–2043, 2015.

T. D’ Orazio, N. Ancona, G. Cicirelli, and M. Nitti, “A ball detection algorithm for real soccer image sequences,” in Proceedings of the International Conference on Pattern Recognition, pp. 210–213, IEEE, Quebec, Canada, August 2002.

X. F. Tong, H. Q. Lu, and Q. S. Liu, “An effective and fast soccer ball detection and tracking method,” in Proceedings of the International Conference on Pattern Recognition, pp. 795–798, IEEE, Cambridge, UK, August 2014.

Z. Kalal, J. Matas, and K. Mikolajczyk, “Online learning of robust object detectors during unstable tracking,” in Proceedings of the International Conference on Computer Vision Workshops, pp. 1417–1424, IEEE, Kyoto, Japan, September 2009.

J. Yu, Y. Tang, and Z. Wang, “Playfield and ball detection in soccer video,” in Proceedings of the International Symposium on Visual Computing, pp. 387–396, Springer, Lake Tahoe, NV, USA, November 2007.

R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, 1960.