Real-Time People Detection and Tracking for Indoor Surveillance Using Multiple Top-View Depth Cameras

被引:0
作者
Tseng, Ting-En [1 ]
Liu, An-Sheng [1 ]
Hsiao, Po-Hao [1 ]
Huang, Cheng-Ming
Fu, Li-Chen
机构
[1] Natl Taiwan Univ, Dept Elect Engn, Taipei, Taiwan
来源
2014 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2014) | 2014年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a real-time indoor surveillance system which installs multiple depth cameras from vertical top-view to track humans. This system leads to a novel framework to solve the traditional challenge of surveillance through tracking of multiple persons, such as severe occlusion, similar appearance, illumination changes, and outline deformation. To cover the entire space of indoor surveillance scene, the image stitching based on the cameras' spatial relation is also utilized. The background subtraction of the stitched top-view image can then be performed to extract the foreground objects in the cluttered environment. The detection scheme including the graph-based segmentation, the head hemiellipsoid model, and the geodesic distance map are cascaded to detect humans. Moreover, the shape feature based on diffusion distance is designed to verify the human tracking hypotheses within particle filter. The experimental results demonstrate the real-time performance and robustness in comparison with several state-of-the-art detection and tracking algorithms.
引用
收藏
页码:4077 / 4082
页数:6
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