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Kinect‐Based Pedestrian Detection for Crowded Scenes
Author(s) -
Chen Xiaofeng,
Henrickson Kristian,
Wang Yinhai
Publication year - 2016
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12163
Subject(s) - pedestrian detection , computer vision , artificial intelligence , pedestrian , computer science , rgb color model , background subtraction , cluster analysis , tracking (education) , pixel , geography , psychology , pedagogy , archaeology
Pedestrian movement data including volumes, walking speeds, and trajectories are essential in transportation engineering, planning, and research. Although traditional image‐based pedestrian detectors provide very rich information, their performance degrades quickly with increased occurrence of occlusion. The three‐dimensional sensing capabilities of Microsoft's Kinect present a potential cost‐effective solution for occlusion‐robust pedestrian detection. This article proposes an efficient pedestrian detection approach for crowded scenes by fusing RGB and depth images from the Kinect. More specifically, we first extract the pedestrian contour regions from RGB images using background subtraction. Then, we develop a region clustering algorithm to extract pedestrians from the contour regions using depth information. Finally, a tracking and counting algorithm is designed to acquire pedestrian volumes. The proposed approach was proven effective with an average detection accuracy of 93.1% at 20 frames per second. These results demonstrate the feasibility of using the low‐cost Kinect device for real‐world pedestrian detection in crowded scenes.