Pedestrian target recognition in multiple videos is a hot issue in video image processing. Many existing recognition algorithms have problems in extracting pedestrian image features, such as multiple dimensions, slow recognition speed and so on. In this paper, a pedestrian recognition method with multiple angles in different videos based on the fusion of HOG, LBP, HSV features and XGBoost classify is proposed. First, use the coefficient variation to reduce the HOG feature dimension; then use LBP to extract the strong expressive features as the local features of the image; then fuse the HOG, LBP and HSV to enhance the ability of pedestrian target features; finally, use the XGBoost algorithm to classify the fusion features, use Cosine similarity to calculate the distance between target and candidates. Experiments show that the proposed method has greatly improved the recognition speed and recognition accuracy in public data sets and selfmade data sets.
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