Stochastic approach based salient moving object detection using kernel density estimation

Peng Tang; Zhifang Liu; Lin Gao; Peng Sheng

doi:10.1117/12.750400

15 November 2007 Stochastic approach based salient moving object detection using kernel density estimation

Peng Tang, Zhifang Liu, Lin Gao, Peng Sheng

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67863P (2007) https://doi.org/10.1117/12.750400
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Background modeling techniques are important for object detection and tracking in video surveillances. Traditional background subtraction approaches are suffered from problems, such as persistent dynamic backgrounds, quick illumination changes, occlusions, noise etc. In this paper, we address the problem of detection and localization of moving objects in a video stream without apperception of background statistics. Three major contributions are presented. First, introducing the sequential Monte Carlo sampling techniques greatly reduce the computation complexity while compromise the expected accuracy. Second, the robust salient motion is considered when resampling the feature points by removing those who do not move in a relative constant velocity and emphasis those in consistent motion. Finally, the proposed joint feature model enforced spatial consistency. Promising results demonstrate the potentials of the proposed algorithm.

Citation Download Citation

Peng Tang, Zhifang Liu, Lin Gao, and Peng Sheng "Stochastic approach based salient moving object detection using kernel density estimation", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67863P (15 November 2007); https://doi.org/10.1117/12.750400

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