会议专题

Infrared Moving Targets Detection Based on Optical Flow Estimation

The optical flow algorithm cannot acquire accuracy motion parameter estimation at low-gradient points. At the same time, the present improved methods required artificial selected parameters and when the threshold value was set too high the object area would yield holes. Two improved optical flow estimation methods were presented by modifying the optical flow basic constraint weighted function. Optical flow is rarely used in infrared image because of the high noise. So the simulations are made on real infrared image sequences. The experiment results demonstrate that the improved methods can depress the repression of reliable optical flow when the threshold value was set too high. The improved methods improve the self-adaptive ability what lay a good foundation for moving object detection and tracking. The optical flow could be used in object segmentation, moving status analysis and target tracking of infrared images.

Optical Flow Gloabal Constraint Local Constraint Self-adaptive weighted function Moving Targets Detection

Yunguang Qi Gang An

Department of Mechanical Engineering The Academy of Armored Forces Engineering Beijing, China, 100072

国际会议

2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

哈尔滨

英文

2452-2455

2011-12-24(万方平台首次上网日期,不代表论文的发表时间)