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Overview: Pedestrian detection is widely used in the field of computer vision, such as public security, intelligent robots, visual surveillance and behavior analysis and so on. However, due to the various factors like complex and changeable environment, different shooting angles, diversity of human behavior, pedestrian detection accuracy and efficiency are not high in the practical application. Therefore, the research of pedestrian detection algorithm is still an important topic in the field of computer vision. Pedestrian detection can generally be considered as the combination of feature extraction with classifier design to automatically detect an existing object from an unknown image or video. With the concept of deep learning proposed, more and more deep learning algorithms have been applied to pedestrian detection. In the pedestrian detection system, the detection mode which combines the HOG features with the LBP features and classifies them with the HIKSVM classifier has been widely used and has achieved good results. HOG features and LBP features have been widely used in feature extraction, at the same time, more and more experts and scholars are also committed to optimizing the existing features. HOG and its improved algorithm obtains good experimental results. However, due to the nature of the gradient, the HOG descriptor is quite sensitive to noise. LBP is a simple but effective operator for describing local image modes. Many of its improved operators are also proposed for extracting the texture features of an image. However, the original LBP and the improved LBP operator are ineffective in extracting the local gray-level difference information, and have the problems of poor robustness to the noise and poor rotation invariance. In order to overcome these shortcomings of the method, this paper proposes a pedestrian detection algorithm based on the feature fusion of CLBC and HOG. First, the CLBC feature of the original image is calculated, and the HOG feature based on the CLBC texture feature spectrum is calculated. The HOG feature of the original image is then calculated to extract the edge feature of the image. Then three features of the image are fused to describe the image, and after that we use principal component analysis to reduce the feature dimension. Finally the detection of the pedestrian is realized by using the HIKSVM classifier. In this paper, experiments are carried out in Caltech pedestrian database and INRIA pedestrian database to verify the effectiveness of the proposed algorithm. The final experimental results show that the proposed algorithm improves the accuracy of pedestrian detection.
Flow chart of extended HOG-CLBC for pedstrain detection
LBC coding principle
CLBC feature extraction flow chart
HOG feature generation process flow chart
HOG feature diagram of pedestrian image
Caltech pedestrian data set test results
Caltech pedestrian data set comparison results
NRIA pedestrian data set test results
INRIA Pedestrian dataset comparison results