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Robust real-time pedestrian detection in surveillance videos

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Abstract

Detecting different categories of objects in an image and video content is one of the fundamental tasks in computer vision research. Pedestrian detection is a hot research topic, with several applications including robotics, surveillance and automotive safety. We address the problem of detecting pedestrians in surveillance videos. In this paper, we present a new feature extraction method based on Multi-scale Center-symmetric Local Binary Pattern operator. All the modules (foreground segmentation, feature pyramid, training, occlusion handling) of our proposed method are introduced with its details about design and implementation. Experiments on CAVIAR and other sequences show that the presented system can detect pedestrians in real-time effectively and accurately in surveillance videos.

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Acknowledgments

This work has been supported by the EU FP7 Programme (FP7-SEC-2011-1) No. 285320 (PROACTIVE project). The research was also partially supported by the Hungarian Scientific Research Fund (No. OTKA 106374).

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Authors and Affiliations

  1. MTA SZTAKI, Computer and Automation Research Institute of the Hungarian Academy of Sciences, Budapest, Hungary

    Domonkos Varga & Tamás Szirányi

  2. Department of Material Handling and Logistics Systems, Budapest University of Technology, Budapest, Hungary

    Tamás Szirányi

Authors
  1. Domonkos Varga
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  2. Tamás Szirányi
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Correspondence to Domonkos Varga.

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Varga, D., Szirányi, T. Robust real-time pedestrian detection in surveillance videos. J Ambient Intell Human Comput 8, 79–85 (2017). https://doi.org/10.1007/s12652-016-0369-0

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