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.