Measuring pedestrian traffic using feature-based regression in the spatiotemporal domain |
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Authors: | Gwang-Gook Lee Whoi-Yul Kim |
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Affiliation: | 1.Intelligent Video Tech. Lab of Emerging Technology R&D Center,Kyeongki-do,Korea;2.Department of Electronic Engineering,Hanyang University,Seoul,Korea |
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Abstract: | Measuring pedestrian traffic in public areas is important for diverse business, security, and building management applications.
Even though various computer vision methods have been proposed for this purpose, they are not suitable for measuring high
traffic levels in large public areas. Because previous methods measured pedestrian traffic by detecting and tracking individuals,
their computational complexity was high and they could not be used for crowded areas. Previous methods were also sometimes
unable to integrate with existing surveillance cameras because they required specific camera angles. We propose an efficient
method for measuring pedestrian traffic that employs feature-based regression in the spatiotemporal domain. The proposed method
first extracts foreground pixels and motion vectors as image features, and then the extracted image features are accumulated
over sequential frames. By identifying relationships between the extracted image features and the number of people passing
by, pedestrian traffic can be measured efficiently. Because the proposed method does not involve any detection and tracking
of humans, its computational complexity is low and the method is less constrained by the angle of the camera. In addition,
due to the statistical nature of the proposed method, it can be used to assess extremely high traffic areas. To evaluate the
proposed method, a dataset consisting of 24 hours of video sequences was prepared. The video data were acquired from 12 different
locations in the most crowded underground shopping mall in Korea. Our studies revealed that the proposed method was capable
of measuring pedestrian traffic with an error rate of 4.46% at an average processing speed of 70 fps. |
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