Using multi-feature fusion and XGBoost to recognize the pedestrian with multiple angles in different videos

ZhanWei Zhang; Murong Jiang; Yaqun Huang; Xinfu Liu; Yingying Zhou

doi:10.1117/12.2623156

16 February 2022 Using multi-feature fusion and XGBoost to recognize the pedestrian with multiple angles in different videos

ZhanWei Zhang, Murong Jiang, Yaqun Huang, Xinfu Liu, Yingying Zhou

Author Affiliations +

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120831Y (2022) https://doi.org/10.1117/12.2623156
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

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.

Citation Download Citation

ZhanWei Zhang, Murong Jiang, Yaqun Huang, Xinfu Liu, and Yingying Zhou "Using multi-feature fusion and XGBoost to recognize the pedestrian with multiple angles in different videos", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120831Y (16 February 2022); https://doi.org/10.1117/12.2623156

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