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We propose a real-time algorithm for recognition of speed limit signs from a moving vehicle. Linear Discriminant Analysis (LDA) required for classification is performed by using Discrete Cosine Transform (DCT) coefficients. To reduce feature dimension in LDA, DCT coefficients are selected by a devised discriminant function derived from information obtained by training. Binarization and thinning are performed on a Region of Interest (ROI) obtained by preprocessing a detected ROI prior to DCT for further reduction of computation time in DCT. This process is performed on a sequence of image frames to increase the hit rate of recognition. Experimental results show that arithmetic operations are reduced by about 60%, while hit rates reach about 100% compared to previous works.

Driver safety is the main concern of the advanced vehicle system which became implementable due to the development of the autonomous driving, automatic control, and imaging technology. An advanced vehicle system gives driver information related to safety by sensing the surroundings automatically [

Several classification algorithms have been proposed, which include Neural Networks [

This paper proposes an efficient real-time algorithm for recognition of speed limit signs by using reduced feature dimension. In this research study, DCT is employed and parts of Discrete Cosine Transform (DCT) coefficients are used as inputs to LDA instead of features extracted from image. DCT coefficients are selected by a devised discriminant function. To further reduce DCT computation time, binarization and thinning are applied to the detected Region of Interest (ROI). Image of speed limit sign in the distance obtained from camera has a low resolution and it gives poor rate of recognition. To resolve this problem, this paper proposes a recognition system using classification results on a sequence of frames. It can enhance hit rate of recognition by accumulating the probability of single frame recognition.

In this section, LDA is briefly described, which is popularly employed for classification. LDA is a classical statistical approach for dimensionality reduction [

Projection of data

The projection shown in Figure _{i} and _{i} is number of data:

It is required to find the axis

Within-class scatter of class

From (

The numerator in (

From (

Optimal

Even though LDA is one of the most popular mathematical models used for classification, it is difficult to be directly used. _{w} term in (

In this paper, a method which can reduce feature dimension effectively without increasing computational complexity is proposed for real-time algorithm for classification of speed limit signs.

As the number of operations in classification process is proportional to the number of data inputs, it is desirable to remove less significant inputs for classification [

Figure

Flowchart of the proposed algorithm.

Since the size of ROI varies with the distance between vehicle and speed limit sign, bicubic interpolation is employed to normalize the size of ROI into a predetermined one. Normalized ROI is converted into gray image to reduce bit width of each pixel, and the area indicating a speed limit is cropped by separating foreground from background. Then, white balancing is performed to reduce brightness variance of obtained image. To improve the resultant quality of auto white balancing, the proposed algorithm uses the white area of speed limit sign as a reference. Figure

An example of ROI preprocessing in the proposed algorithm. (a) Input ROI, (b) normalized ROI, (c) gray image, (d) cropped image, (e) white balanced image, (f) binarized image, and (g) image after thinning.

Prior to DCT computation, binarization and thinning are performed in the proposed algorithm. DCT computation uses each of pixel values to obtain coefficients, which require a large amount of operations for usage in real-time recognition. By using 1-bit pixels obtained by binarization, the time for multiplication can be significantly reduced. The threshold of binarization is set to 128, middle value of grayscale image, since the brightness variance has been compensated by applying white balance in advance. Figure

For further reduction of DCT computation time, thinning [

2D DCT computation can be replaced by two 1D DCT computations using the row-column decomposition [

Classifier’s performance increases dependently on the number of features. However, computational complexity and memory requirements are proportional to the number of the features both in the learning and in the classification processes. To reduce these burdens we need to remove less significant features [

First, mean of DCT coefficients _{c}(

Here, ^{k}(

Classification is more efficient when samples in the same class are clustered together and samples belonging to different classes are scattered in the feature space. The larger the discriminant factors are, the greater the impact on classification is in the field. The devised discriminant function selects a number of indices of 2D DCT coefficients in descending order which have large DF values. Those selected indices are used as reference positions whose corresponding DCT coefficients will be applied in classification process.

Classification is performed using the Linear Discriminant Analysis (LDA) and Mahalanobis distances [_{c}(_{c} is the highest from _{1} to _{N}.

Images used for training and classification were captured on road using a mirrorless camera (MOS sensor, 4/3 inch) mounted with a 20 mm lens at

Experimental results of hit rates of recognition.

Speed (km/h) | Number of images | ||
---|---|---|---|

3 | 7 | 9 | |

20 | 88.0% | 95.7% | 100.0% |

30 | 92.0% | 100.0% | 100.0% |

40 | 96.0% | 97.8% | 100.0% |

50 | 92.0% | 100.0% | 100.0% |

60 | 90.0% | 95.6% | 100.0% |

70 | 100.0% | 97.8% | 100.0% |

80 | 90.0% | 100.0% | 100.0% |

90 | 98.0% | 100.0% | 100.0% |

100 | 98.0% | 100.0% | 100.0% |

110 | 94.0% | 100.0% | 100.0% |

| |||

Average | 93.8% | 98.7% | 100.0% |

The hit rates of recognition are about 100% when classification is performed for 7~9 consecutive images. Table

Experimental results of number of arithmetic operations.

Operations | Methods | ||
---|---|---|---|

LDA | SVM | Proposed (comparison) | |

Add | 4,000 | 9,697 | 1,570 (−60.7%/−83.8%) |

Multiplication | 3,990 | 7,297 | 1,731 (−56.6%/−76.2%) |

A real-time algorithm for speed limit sign recognition has been proposed with reduced amount of operations using DCT. The number of arithmetic operations was reduced by using lookup table on binarized image, which was obtained through binarization and thinning. To reduce feature dimension, discriminant function which selects parts of DCT coefficients was devised. Selection of DCT coefficients makes it possible to reduce runtime for recognition.

Accurate recognition of speed limit signs in low resolutions or in the distance is achievable by applying the proposed algorithm.

This research was supported by the MEST (Ministry of Education, Science and Technology), through NRF (National Research Foundation) of Korea under Grant no. 2012-0002586.