Pedestrian Counting System Based on Multiple Object Detection and Tracking

  • Xiang Li
  • Haohua Zhao
  • Liqing ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)


With the increasing demands on video surveillance and business promotion, effective pedestrian counting in surveillance environments has become a hot research topic in computer vision. In this paper, we implement a pedestrian counting system based on multiple object detection and tracking. Region proposal network (RPN) and Real Adaboost classifier are employed to train a head-shoulder detector with high accuracy. We utilize the DSST algorithm to track the position transformations and the size changes of pedestrians. By combining human detection with object tracking together and using detection results to optimize the tracking algorithm, the pedestrian counting system is developed with high robustness against occlusions. We evaluated the system on the videos recorded in the subway station. The results showed that our system achieves a high accuracy and can be used for pedestrian counting in crowded public places.


Pedestrian counting Human detection Object tracking 



The work was supported by the National Natural Science Foundation of China (Grant No. 91420302), the National Basic Research Program of China (Grant No. 2015CB856004) and the Key Basic Research Program of Shanghai, China (15JC1400103).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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