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	Real-time Human Motion Estimation for Human Robot Collaboration 会议论文 OAI收割 Tianjin, China, July 19-23, 2018 作者: Zhang YA(张延安); Ren HL(任恒乐); Zou FS(邹风山); Xu F(徐方); Jia K(贾凯) \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2019/06/18 human robot collaboration GMM EM real-time motion estimation minimum jerk
	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：60/0 \| 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).
	Optimization on motion estimation algorithm based on H. 264 (EI CONFERENCE) 会议论文 OAI收割 2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, August 20, 2010 - August 22, 2010, Chengdu, China Wen X.; Li G. 收藏 \| 浏览/下载：56/0 \| 提交时间：2013/03/25 Motion estimation is a very important part of video compression. As a result of using precision of motion vector in H.264 encoder the computational cost increases rapidly and motion estimation is the most time-consuming stage. In this paper based on the UMHexagonS algorithm an optimized algorithm is proposed based on the dynamic search window selection big hexagon and small hexagon search mode respectively which saves motion estimation time effectively with a little quality loss. Experiments with some typical video sequences show that compared to the original UMHexagonS algorithm this new algorithm can save about 17.851 % motion estimation time and reduce the complexity of original scheme as well as enhance the real time performance of encoder and almost has no changes in the reconstructed picture quality and bitrates. 2010 IEEE.