Recent advances in pedestrian detection, a fundamental problem in computer vision, have been attained by transferring the learned features of convolutional neural networks (CNN) to pedestrians. However, existing methods often show a significant drop in performance when heavy occlusion and deformation happen because most methods rely on holistic modeling. Unlike most previous deep models that directly learn a holistic detector, we introduce the semantic part information for learning the pedestrian detector. Rather than defining semantic parts manually, we detect key points of each pedestrian proposal and then extract six semantic parts according to the predicted key points, e.g., head, upper-body, left/right arms and legs. Then, we crop and resize the semantic parts and pad them with the original proposal images. The padded images containing semantic part information are passed through CNN for further classification. Extensive experiments demonstrate the effectiveness of adding semantic part information, which achieves superior performance on the Caltech benchmark dataset.
|Title of host publication
|Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
|IEEE Computer Society
|Number of pages
|Published - Jun 2019
|32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: Jun 16 2019 → Jun 20 2019
|IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
|32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
|06/16/19 → 06/20/19
Bibliographical noteFunding Information:
Acknowledgments This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research and by Natural Science Foundation of China, Grant No. 61603372.
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering