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	Robust Object Tracking via Information Theoretic Measures 期刊论文 OAI收割 International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 5, 页码: 652-666 作者: Wei-Ning Wang; Qi Li; Liang Wang \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2021/02/22 Object tracking information theoretic measures correntropy template update robust to complex noises.
	Online structured sparse learning with labeled information for robust object tracking 期刊论文 OAI收割 Journal of Electronic Imaging, 2017, 卷号: 26, 期号: 1, 页码: 1-16 作者: Fan BJ(范保杰); Cong Y(丛杨); Tang YD(唐延东) 收藏 \| 浏览/下载：28/0 \| 提交时间：2017/03/07 robust object tracking online dictionary learning and updating robust sparse coding prior information joint decision metric
	Discriminative Object Tracking via Sparse Representation and Online Dictionary Learning 期刊论文 OAI收割 IEEE Transactions on Cybernetics, 2013, 期号: 44, 页码: 539-553 作者: Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2020/05/26 Dictionary Learning Object Tracking Robust Keypoints Matching Sparse Representation.
	Study particle filter tracking and detection algorithms based on DSP signal processors (EI CONFERENCE) 会议论文 OAI收割 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：20/0 \| 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 OAI收割 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：20/0 \| 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.
	Integrated intensity, orientation code and spatial information for robust tracking (EI CONFERENCE) 会议论文 OAI收割 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, May 23, 2007 - May 25, 2007, Harbin, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 real-time tracking is an important topic in computer vision. Conventional single cue algorithms typically fail outside limited tracking conditions. Integration of multimodal visual cues with complementary failure modes allows tracking to continue despite losing individual cues. In this paper we combine intensity orientation codes and special information to form a new intensity-orientation codes-special (IOS) feature to represent the target. The intensity feature is not affected by the shape variance of object and has good stability. Orientation codes matching is robust for searching object in cluttered environments even in the cases of illumination fluctuations resulting from shadowing or highlighting etc The spatial locations of the pixels are used which allow us to take into account the spatial information which is lost in traditional histogram. Histograms of intensity orientation codes and spatial information are employed for represent the target Mean shift algorithm is a nonparametric density estimation method. The fast and optimal mode matching can be achieved by this method. In order to reduce the compute time we use the mean shift procedure to reach the target localization. Experiment results show that the new method can successfully cope with clutter partial occlusions illumination change and target variations such as scale and rotation. The computational complexity is very low. If the size of the target is 3628 pixels it only needs 12ms to complete the method. 2007 IEEE.