Kinect‐Based Pedestrian Detection for Crowded Scenes

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.