Grid feature and visualizition for occlusion perception in object tracking

被引:0
作者
机构
[1] Northeast Dianli University, Academy of Information Engineer, Jilin
[2] Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences
[3] Beihang University, Academy of Computer Science
来源
Meng, B. (mengbo_nannan@163.com) | 2013年 / Chinese Optical Society卷 / 42期
关键词
Grid feature; Object tracking; Occlusion judgment; Sub-model matching; Vision perception; Visualization;
D O I
10.3788/gzxb20134201.0098
中图分类号
学科分类号
摘要
A grid-based histogram of the orientation gradient feature and visualization technique were used in object tracking algorithm to solve the problem of tracking objects moving in surveillance video. The histogram of orientation gradient based on grid was designed as the local feature to describe the object. And it can apperceive the abnormalities of objects such as occlusion, disturbance and deformation etc. The visualization was used to real-time observe the changes of the characteristics through the changes of the local feature. The weights of the sub-model was adjusted adaptively to keep the stability of the tracking algorithm. The experimental results show that the proposed local feature can perceive the abnormity correctly, and can impove the robustness and stability of the tracking algorithm. The self-perception matching algorithm based on this local feature can tracking object accurately and stably in surveillance video.
引用
收藏
页码:98 / 103
页数:5
相关论文
共 10 条
[1]  
Oliva A., Gist of the scene, pp. 251-256, (2005)
[2]  
(2006)
[3]  
Qian L., Researches on image understanding based on hierarchical visual perception mechnaisms, (2009)
[4]  
Dong L.-G., Tao L.-M., Xu G.-Y., Head pose estimation based on a second order histogram of the orientation gradient, Journal of Tsinghua University(Science and Technology), 51, 1, pp. 73-79, (2011)
[5]  
Deco G., Schurmann B., A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recogniton, Vision Research, 40, pp. 2845-2859, (2000)
[6]  
Amit Y., Mascaro M., An integrated network for invariant visual detection and recognition, Vision Research, 43, pp. 2073-2088, (2003)
[7]  
Dalai N., Triggs B., Histograms of oriented gradients for hunman detection, Proc IEEE Conf Computer Vision and Pattern Recognition, pp. 886-893, (2005)
[8]  
Wang H.-L., Zhang M.-X., Dian H.-F., Application of the theory of D-S evidence in target identification, Automation & Instrumentation, 7, pp. 14-17, (2011)
[9]  
Grossberg S., Swaminathan G., A laminar cortical model for 3D perception of slanted and curved surfaces and of 2D images: Development, attention, and bistability, Vision Research, 44, 11, pp. 1147-1187, (2004)
[10]  
Meng B., Research and application of the local optimal particle filter target tracking algorith, (2008)