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Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

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Abstract

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Souk-Ahras, Souk-Ahras, Algeria

    Imed Bouchrika

  2. Department of Electronics and Computer Science, University of Southampton, SO17 2BJ, Southampton, United Kingdom

    John N. Carter & Mark S. Nixon

Authors
  1. Imed Bouchrika
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  2. John N. Carter
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  3. Mark S. Nixon
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Correspondence to Imed Bouchrika.

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Bouchrika, I., Carter, J.N. & Nixon, M.S. Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras. Multimed Tools Appl 75, 1201–1221 (2016). https://doi.org/10.1007/s11042-014-2364-9

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  • DOI: https://doi.org/10.1007/s11042-014-2364-9

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