People detection in low-resolution video with non-stationary background

被引:7
|
作者
Zhang, Jianguo [1 ]
Gong, Shaogang [2 ]
机构
[1] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland
[2] Queen Mary Univ London, Dept Comp Sci, London E1 4NS, England
基金
英国工程与自然科学研究理事会;
关键词
Visual surveillance; People detection; Bayesian fusion; Long term motion; AdaBoost; MOTION;
D O I
10.1016/j.imavis.2008.06.013
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we present a framework for robust people detection in low resolution image sequences of highly cluttered dynamic scenes with non-stationary background. Our model utilizes appearance features together with short- and long-term motion information. In particular, we boost Integral Gradient Orientation histograms of appearance and short-term motion. Outputs from the detector are maintained by a tracker to correct any misdetections. A Bayesian model is then deployed to further fuse long-term motion information based on correlation. Experiments show that our model is more robust with better detection rate compared to the model of Viola et al. [Michael J. Jones Paul Viola, Daniel Snow, Detecting pedestrians using patterns of motion and appearance, International journal of Computer Vision 63(2) (2005) 153-161]. (C) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码:437 / 443
页数:7
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