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A robust system for real-time pedestrian detection and tracking

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Abstract

A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.

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Authors and Affiliations

  1. Key Laboratory for Urban Transportation Complex Systems Theory and Technology of Ministry of Education (Beijing Jiaotong University), Beijing, 100000, China

    Qi Li  (李琦), Chun-fu Shao  (邵春福) & Yi Zhao  (赵熠)

Authors
  1. Qi Li  (李琦)
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  2. Chun-fu Shao  (邵春福)
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  3. Yi Zhao  (赵熠)
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Corresponding author

Correspondence to Chun-fu Shao  (邵春福).

Additional information

Foundation item: Project(50778015) supported by the National Natural Science Foundation of China; Project(2012CB725403) supported by the Major State Basic Research Development Program of China

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Li, Q., Shao, Cf. & Zhao, Y. A robust system for real-time pedestrian detection and tracking. J. Cent. South Univ. 21, 1643–1653 (2014). https://doi.org/10.1007/s11771-014-2106-1

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  • DOI: https://doi.org/10.1007/s11771-014-2106-1

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