中国开放科研知识云: 检索

An Adaptive Two-Scale Image Fusion of visible and Infrared Images 期刊论文
Ieee Access, 2019, 卷号: 7, 页码: 56341-56352
作者: X.Y.Han; T.Lv; X.Y.Song; T.Nie; H.D.Liang

收藏 | 浏览/下载：1/0 | 提交时间：2020/08/24

Fusion global-local-topology particle swarm optimization (FGLT-PSO),guided filter,image fusion

T-S tracking algorithm based on context auxiliary feature 期刊论文
Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2018, 卷号: 26, 期号: 8, 页码: 2122-2131
作者: Song, Ce; Zhang, Bao; Song, Yu-Long; Qian, Feng

收藏 | 浏览/下载：5/0 | 提交时间：2019/09/17

Target tracking Dynamic models Monte Carlo methods Passive filters Pixels Statistical tests

基于特征匹配的机载电子稳像技术研究 学位论文
博士: 中国科学院大学, 2015
作者: 吉淑娇

收藏 | 浏览/下载：18/0 | 提交时间：2015/11/30

特征匹配运动滤波二进制算子特征点分类

Target tracking based on incremental deep learning 期刊论文
Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2015, 卷号: 23, 期号: 4, 页码: 1161-1170
作者: Cheng, S.; J.-X. Sun; Y.-G. Cao and L.-R. Zhao

收藏 | 浏览/下载：13/0 | 提交时间：2016/08/24

序列图像红外小目标检测与跟踪算法研究 学位论文
博士: 中国科学院大学, 2014
孙继刚

收藏 | 浏览/下载：167/0 | 提交时间：2014/08/21

The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
Sun H.; Han H.-X.

收藏 | 浏览/下载：50/0 | 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).

Study particle filter tracking and detection algorithms based on DSP signal processors (EI CONFERENCE) 会议论文
2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China
Dong Y.; Chuan W.

收藏 | 浏览/下载：12/0 | 提交时间：2013/03/25

In Video tracking detection and tracking usually need two algorithms. The process is complex and need much time which detection and tracking are. In this paper a hybrid valued sequential state vector is formulated. The state vector is characterized by information of target appearance flag and of location. Particle filter-based method implements detection and tracking at one time. In order to reduce process time and think of pixel position in tracking field feature histogram of luminance is as observe vector and used posterior estimate. In this paper the luminance component is derived and target is recognized and tracked through image processor based on DSP in order to implementing real-time. The experimental results confirm that method can detect and track the object in real-time successfully when the number of particles is 160. The method is robust for rolling scale and partial occlusion. 2010 IEEE.

Study on color image tracking and detection algorithms based on particle filter (EI CONFERENCE) 会议论文
International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, June 17, 2009 - June 19, 2009, Beijing, China
Wu C.; Sun H.-J.; Yang D.

收藏 | 浏览/下载：14/0 | 提交时间：2013/03/25

In Video tracking detection and tracking need two algorithms. The process is complex and need much time which detection and tracking is. In this paper a hybrid valued sequential state vector is formulated. The state vector is characterized by information of target appearance and of location. Particle filter-based method implements detection and tracking. In order to reduce process time and think of pixel position in tracking field feature histogram of color-based is as observe vector and used posterior estimate. The experimental results confirm that method can detect and track object in 17.68ms successfully when the number of particles is 160. The method is robust for rolling scale and partial occlusion. 2009 SPIE.

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