Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.
Underwater object detection is aiming to extract the interesting objects from the background scene. Effective underwater moving object detection contributes to many scientific research and engineering applications, such as marine biology, seabed topography, marine environment monitoring, and marine exploration [
After a long period of evolution, biological visual systems develop a strong ability for sensing the world. Various visual mechanisms in animals have been simulated and introduced into computer vision tasks [
In order to solve the problems in existing underwater moving object detection method, this paper proposes a novel hierarchical background model by simulating the frog visual perception, which is considered to have an excellent ability for motion detection [
The remainder of this paper is organized as follows. Section
Frog is a typical visually guided animal. The eye of the frogs is their main biological sensor for tracking preys. However frogs are more sensitive to the moving object compared with other animals. When frogs keep completely static, nothing can be perceived by the retina of the eye. Therefore they are blind to the static object even if it is very close [
In the view of the underwater moving object detection, this paper focuses on three aspects in frog’s visual and neurophysiologic mechanism. The low distance resolution would result in the blurred background and clear foreground presented in the retina. Therefore frog can easily distinguish the object in the foreground from the background. By employing this mechanism in the computer vision, we firstly segment the image into subblocks which are utilized for classification of the foreground and the background. Then the background region is ignored and the pixel-based processing is operated on the foreground region. With the above preprocessing, the foreground region can be easily extracted and the object detection operation is focused on the foreground region. Accordingly the redundant computation on the background region is saved and the complexity of the object detection is reduced. Furthermore the subblocks based processing solves the difficulties caused by nonstationary background in some extent. A frog has a memory on both the moving objects and the background. Once the interest is focused on any objects, the attention of frogs can hardly be dispersed. By taking this into consideration, the foreground and the background are modelled and updated by the feature extracted in the respective regions. This strategy solves the problems caused by the change in the lamination and increases the accuracy of underwater object detection. The retina and neural fiber in the eye of frogs are sensitive to the local bright-dark contrast and the bright and dark change in movement region. This visual sensitivity can be modeled by the selection of the image feature. According to the computer vision task, the intensity and the texture feature describing the intensity distribution in local regions are extracted for detecting the contour of the moving object.
Inspired by the above aspects of visual mechanisms in the eye of frogs, this paper proposed a hierarchical background model based underwater moving object detection method. In this method, the foreground and the background are modeled by the information extracted from pixels and subblocks, respectively. The intensity and the texture feature are extracted to describe the contour of the underwater objects correctly.
The key for the object extraction is to stretch the contrast between the object and the background. Considering the spatial correlation between pixels, the subblock based background modeling is sensitive to the global change of the scene but blind to local movement which solves the problems caused by the unstable background. However it might generate the rough object region with serious blocking effect due to subblock operation which may deform the object and the intensity feature for modeling the background can hardly identify the objects in the scene in some cases.
More precise object information can be extracted by using pixel-based background model. By using the pixel-based operation, the rough object region is correctly detected without the blocking effect. However, the results given by the pixel-based operation do not only include the object region but also include the regions surrounding the object. Hence, errors would exist in the scene with the unstable background.
Therefore, the subblock and the pixel-based operation are mutually compensative. The asymmetric forward feedback mechanism is then applied to jointly combine these two strategies to form a hierarchical background model for object detection. Firstly, intensity features are extracted in the subblock and the difference between the subblocks is taken as the cue for classifying the rough object and background region. The rough object region is extracted afterwards and the background model is updated. Then texture features of every single pixel which belongs to the rough object region are extracted to establish the pixel-based background model. Figure
Block diagram of the proposed underwater moving object detection method.
In order to reduce the computational complexity, the detection process is operated under the following rules. The background region identified by the subblock based method is reliable. The pixel-based identification is omitted for the given background region. In order to adapt our method to the change of the scene, the background region is updated by the information extracted from the subblock regions but not the pixels. The foreground region identified by the subblock based method contains the pixels of the real object region and a small amount of unstable pixels. Hence, the pixel-based algorithm should be utilized to further detect the object region to remove the blocking effect. Since most of the pixels in the detected rough object region are included in the real object region, updating process of the background model is not necessary in this region.
The rough object region is detected by the subblock based operation. The input video frames are segmented into multiple nonoverlapped subblocks with a size of
The feature extracted in a subblock is represented by a vector
A set of intensity feature vectors
To extract the rough object region, the intensity features in the subblocks are extracted. Then they are related to the background model by the Euclidean distance:
If the feature of subblock
If subblock
For each pixel in the detected rough object region, the texture feature is extracted and utilized to extract the accurate object contour. In this paper, we choose the local binary pattern (LBP) texture operator to describe texture features. The most important properties of the LBP operator are its tolerance against the change of illumination and its computational simplicity [
Given the center pixel
Neighborhood with different
By introducing the difference between
Assuming that
As
If illumination changes linearly in underwater scenes, the value of
Practically the sign of the differences in a neighborhood is interpreted as a
LBP is robust against the considerable gray-scale variations which commonly appear in natural images. Moreover, the LBP operator is computationally economic, which is important in practice. Besides these factors, LBP is a nonparametric method without any assumptions about the underlying distributions. However since the low change of the grey in the underwater background, the grey values between the center point and its neighborhood are homogeneous. In this case, a large error would exist if the traditional LBP operator is used. For example, if
A set of texture feature vectors
In order to demonstrate the efficiency of the proposed method for detecting underwater moving object, the classic Gaussian background modeling method is selected as the reference which is used to compare it with our proposed method. The detection results are shown in Figures
Results of close moving object detection. (a) Original images. (b) Gaussian background modeling method. (c) The proposed method.
Results of distant moving object detection. (a) Original images. (b) Gaussian background modeling method. (c) The proposed method.
Results of multiple moving object detection. (a) Original images. (b) Gaussian background modeling method. (c) The proposed method.
According to the detection results, the Gaussian background modeling method has the ability to roughly detect contours of object. However the detected contours are not complete, especially for those parts which are similar to the background. In contrast to the results given by the Gaussian method, the contours of objects given by the proposed hierarchical method are more complete. The detected results are more precise. The criteria
Performance comparison.
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Mean value | |||||
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Gaussian background modeling method | 0.9713 | 0.0183 | 0.8660 | 0.0091 | 0.9657 | 0.0213 | 0.9343 | 0.0162 |
Hierarchical background modeling method | 0.9906 | 0.0092 | 0.9589 | 0.0082 | 0.9881 | 0.0107 | 0.9792 | 0.0094 |
From the results shown in Table
Inspired by the frog visual mechanism, the frog visual information processing mode is simulated to establish a bionic underwater object detecting method. By using the illumination information of the input image a hierarchical background model is established to detect underwater moving objects. The experimental results demonstrate that the proposed method detects underwater moving objects effectively and accurately. In this paper the visual mechanism in the visual system of frogs is modeled preliminarily and further research work will focus on this field to achieve a more complete bionic model.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work is supported by the National Natural Science Foundation of China (no. 61263029).