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
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)