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Multi-view and multi-plane data fusion for effective pedestrian detection in intelligent visual surveillance

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Abstract

For the robust detection of pedestrians in intelligent video surveillance, an approach to multi-view and multi-plane data fusion is proposed. Through the estimated homography, foreground regions are projected from multiple camera views to a reference view. To identify false-positive detections caused by foreground intersections of non-corresponding objects, the homographic transformations for a set of parallel planes, which are from the head plane to the ground, are applied. Multiple features including occupancy information and colour cues are extracted from such planes for joint decision-making. Experimental results on real world sequences have demonstrated the good performance of the proposed approach in pedestrian detection for intelligent visual surveillance.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 60975082 and the Scientific Research Program funded by Shaanxi Provincial Education Department, P. R. China, under Grant 15JK1310.

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Authors and Affiliations

  1. College of Electronics and Information, Xi’an Polytechnic University, Xi’an, 710048, China

    Jie Ren

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China

    Ming Xu

  3. Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3BX, UK

    Ming Xu & Jeremy S. Smith

  4. Division of Computer Science, University of Nottingham Ningbo, Ningbo, 315100, China

    Shi Cheng

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  1. Jie Ren
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  2. Ming Xu
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  3. Jeremy S. Smith
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  4. Shi Cheng
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Correspondence to Jie Ren.

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Ren, J., Xu, M., Smith, J.S. et al. Multi-view and multi-plane data fusion for effective pedestrian detection in intelligent visual surveillance. Multidim Syst Sign Process 27, 1007–1029 (2016). https://doi.org/10.1007/s11045-016-0428-x

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  • DOI: https://doi.org/10.1007/s11045-016-0428-x

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