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	A Hybrid Approach for Multiple Particle Tracking Microrheology 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2013, 卷号: 10 作者: Xie, Liangjun; Gu, Nong; Cao, Zhiqiang; Li, Dalong 收藏 \| 浏览/下载：29/0 \| 提交时间：2015/08/12 Multiple Particle Tracking Microrheology Circular Hough Transform Circle Detection
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：59/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	Multiple Object Tracking Via Species-Based Particle Swarm Optimization 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2010, 卷号: 20, 期号: 11, 页码: 1590-1602 作者: Zhang, Xiaoqin; Hu, Weiming; Qu, Wei; Maybank, Steve 收藏 \| 浏览/下载：12/0 \| 提交时间：2015/08/12 Multiple object tracking particle swarm optimization
	Occlusion Reasoning for Tracking Multiple People 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2009, 卷号: 19, 期号: 1, 页码: 114-121 作者: Hu, Weiming; Zhou, Xue; Hu, Min; Maybank, Steve 收藏 \| 浏览/下载：40/0 \| 提交时间：2015/08/12 Occlusion reasoning particle filtering tracking multiple people visual surveillance
	Contour extracting with combination particle filtering and em algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging, ISPDI 2007: Related Technologies and Applications, September 9, 2007 - September 12, 2007, Beijing, China Meng B.; Zhu M. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 The problem of extracting continuous structures from images is a difficult issue in early pattern recognition and image processings[1]. Tracking with contours in a filtering framework requires a dynamical model for prediction. Recently Particle filter is widely used because its multiple hypotheses and versatility within framework. However the good choice of the propagation function is still its main problem. In this paper an improved particle filter EM-PF algorithm is proposed which using the EM (Expectation-Maximization) algorithm to learn the dynamical models. The EM algorithm can explicitly learn the parameters of the dynamical models from training sequences. The advantage of using the EM algorithm in particle filter is that it is capable of improve tracking contour by having accurate model parameters. Though the experiment results we show how our EM-PF can be applied to produces more robust and accurate extracting.