ICRA 2011 Paper Abstract


Paper WeP114.2

Shao, Jie (Tongji University), Jia, Zhen (United Technologies Research Center), Li, Zhipeng (Tongji University), Liu, Fuqiang (Tongji University), Zhao, Jianwei (United Technologies Research Center), Peng, Pei-Yuan (United Technologies Research Center)

Spatio-Temporal Energy Modeling for Foreground Segmentation for Multiple Objects Tracking

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation III" (WeP114), Wednesday, May 11, 2011, 13:55−14:10, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 5, 2020

Keywords Computer Vision for Robotics and Automation, Visual Tracking, Surveillance Systems


In this paper, we introduce spatiotemporal energy modeling for foreground segmentation in multiple object tracking, a high accuracy and real-time foreground target extraction algorithm. From a single video sequence with multiple moving objects and stationary background, our algorithm combines spatial (color distribution) and temporal (variety between two consecutive frames) information to extract foreground objects accurately and efficiently. The key idea of our method is to employ tracking results as feedback cues for target detection in the next frame, which adaptively updates the weights and threshold. Using spatiotemporal energy modeling, the foreground extraction errors caused by ambiguous colors in foreground and background boundary and abnormal movements can be substantially reduced. Experimental results of complex scenario video demonstrate the effectiveness of our algorithm.



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