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

来源：

IMAGE AND VISION COMPUTING | 2009年 / 27卷 / 04期

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

页码：437 / 443

页数：7