A Reliable People Counting System via Multiple Cameras

  • Authors:

  • Huadong Ma;Chengbin Zeng;Charles X. Ling

  • Affiliations:

  • Beijing University of Posts and Telecommunications;Beijing University of Posts and Telecommunications;Beijing University of Posts and Telecommunications/The University of Western Ontario

  • Venue:
  • ACM Transactions on Intelligent Systems and Technology (TIST)
  • Year:
  • 2012

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Abstract

Reliable and real-time people counting is crucial in many applications. Most previous works can only count moving people from a single camera, which cannot count still people or can fail badly when there is a crowd (i.e., heavy occlusion occurs). In this article, we build a system for robust and fast people counting under occlusion through multiple cameras. To improve the reliability of human detection from a single camera, we use a dimensionality reduction method on the multilevel edge and texture features to handle the large variations in human appearance and poses. To accelerate the detection speed, we propose a novel two-stage cascade-of-rejectors method. To handle the heavy occlusion in crowded scenes, we present a fusion method with error tolerance to combine human detection from multiple cameras. To improve the speed and accuracy of moving people counting, we combine our multiview fusion detection method with particle tracking to count the number of people moving in/out the camera view (“border control”). Extensive experiments and analyses show that our method outperforms state-of-the-art techniques in single- and multicamera datasets for both speed and reliability. We also design a deployed system for fast and reliable people (still or moving) counting by using multiple cameras.