Acoustics, Speech, and Signal Processing, IEEE International Conference on (2009)
Apr. 19, 2009 to Apr. 24, 2009
Peng Zhang , School of Computer Engineering, Nanyang Technological University, Singapore, 639798
Sabu Emmanuel , School of Computer Engineering, Nanyang Technological University, Singapore, 639798
Pradeep K. Atrey , Department of Applied Computer Science, The University of Winnipeg, 515 Portage Avenue, Canada
Mohan S. Kankanhalli , School of Computing, National University of Singapore, Singapore, 117590
Effective and robust visual tracking is one of the most important tasks for the intelligent visual surveillance. In this paper, we proposed a novel method for detecting and tracking moving people using the spatiotemporal latent semantic cues and the incremental eigenspace tracking techniques. During tracking process, the target appearance model is incrementally learned in low dimensional tensor eigenspace by adaptively updating the eigenbasis and sample mean. At the same time, the spatiotemporal latent semantic cues calibrate the estimation of tracking and detect new moving people coming in the same surveillance scene. Experiment results show that with the calibration based on spatiotemporal latent semantic cues, the proposed method can track the moving people automatically and effectively.
S. Emmanuel, Peng Zhang, P. K. Atrey and M. S. Kankanhalli, "Spatiotemporal latent semantic cues for moving people tracking," Acoustics, Speech, and Signal Processing, IEEE International Conference on(ICASSP), Taipei, Taiwan, 2009, pp. 3533-3536.