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Semantic Structural and Occlusive Feature Fusion for Pedestrian Detection

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PRICAI 2021: Trends in Artificial Intelligence (PRICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13031))

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Abstract

Pedestrian detection under occlusion scenes remains a formidable challenge in computer vision. Recently, anchor-free approach has been raised on the object detection and pedestrian detection field, anchor-free detector Center and Scale Prediction (CSP) has been proposed in pedestrian detection without special measures for occlusion. In this paper, we propose an anchor-free detector named OCSP with power ful occlusion handling ability to existing anchor-free detection network. OCSP integrates prior information into the network to handle occlusion. This high-fusion prior information gives the detector a hint about identifying the structural features of pedestrians. OCSP becomes more robust with the prior information which fusion the semantic head, the visible part, and the size and center body for each pedestrian. The detector enhances the perception of occlusion by predicting the semantic head and visible part. Besides, we design a head branch and add predicting the visible box to achieve a similar result. Experiments show that this fusion of prior information represents a suitable combination. We compare our OCSP with state-of-arts models on the Citypersons dataset, the proposed OCSP detector achieves the state-of-arts result on the CityPersons benchmark.

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Acknowledgments

This work was supported by the National Nature Science Foundation of China (61841602, 61806024), the Jilin Province Education Department Scientific Research Planning Foundation of China (JJKH20210753KJ, JJKH20200618KJ).

Author information

Authors and Affiliations

  1. The College of Computer Science and Engineering, Changchun University of Technology, Changchun, 130012, China

    Hui Wang, Yu Zhang, Ning Wei & Zhongyu Xu

  2. The School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, 130012, China

    Hongchang Ke

  3. National and Local Joint Engineering Research Center for Intelligent Distribution Network Measurement Organization Control and Safe Operation Technology of National Development and Reform Commission, Changchun Institute of Technology, Changchun, 130012, China

    Hongchang Ke

Authors
  1. Hui Wang
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  2. Yu Zhang
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  3. Hongchang Ke
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  4. Ning Wei
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  5. Zhongyu Xu
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Editor information

Editors and Affiliations

  1. MIMOS Berhad, Kuala Lumpur, Malaysia

    Duc Nghia Pham

  2. Sirindhorn International Institute of Science and Technology, Thammasat University, Mueang Pathum Thani, Thailand

    Thanaruk Theeramunkong

  3. Data61, CSIRO, Brisbane, QLD, Australia

    Guido Governatori

  4. Department of Philosophy, Tsinghua University, Beijing, China

    Fenrong Liu

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Wang, H., Zhang, Y., Ke, H., Wei, N., Xu, Z. (2021). Semantic Structural and Occlusive Feature Fusion for Pedestrian Detection. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_22

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  • DOI: https://doi.org/10.1007/978-3-030-89188-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89187-9

  • Online ISBN: 978-3-030-89188-6

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