Motion object detection based on optical flow field and level set
基于光流场与水平集的运动目标检测

Information of optical flow and epipolar constraint were used to get the initial segmentation region of motion object. The value of motion speed and the angle value of optical flow were extracted and feature vector was constructed using these two features. K-means cluster algorithm was used to get the region of motion object and level set was used to get information of image spatial segmentation. Minimization of the energy function led to the optimal segmentation of moving object by curve evolution. Evolution curve stopped at the position that spatial gradient was great and the close curve of moving object was produced. The experimental results show that the method can detect the whole motion object from image sequence.