Visual attention based motion object detection and trajectory tracking

Authors:
Wen Guo;Changsheng Xu;Songde Ma;Min Xu
Affiliations:
National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China and Shandong Institutes of Business and Technology, Electronic Engineering Department, Yan ...;National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia
Venue:
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Year:
2010

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Abstract

A motion trajectory tracking method using a novel visual attention model and kernel density estimation is proposed in this paper. As a crucial step, moving objects detection is based on visual attention. The visual attention model is built by combination of the static and motion feature attention map and a Karhunen-Loeve transform (KLT) distribution map. Since the visual attention analysis is conducted on object level instead of pixel level, the proposed method can detect any kinds of motion objects provided saliency without the affection of objects appearance and surrounding circumstance. After locating the region of moving object, the kernel density is estimated for trajectory tracking. The experimental results show that the proposed method is promising for moving objects detection and trajectory tracking.