Research On Moving Objects Detection In Dynamic Image Sequences

The technology of object detection and tracking is one of the hotspots in the field of computer vision, which is also the basic and important technology in the application of smart surveillance, human-machine interface, mobile Robert navigation, full-sight caterpillar system and so on. Dynamic image sequences possess not only spatial properties like images (such as color, texture etc), but also temporal properties, namely, the motion information. Thus, compared with the traditional image analysis techniques, motion information analysis in image sequences plays more important role in solving the above mentioned problems. However, in the practical applications, jittering camera, moving objects'irregular motion, the moving background caused by camera motion etc make the detection of moving objects difficult. A thorough research on the detection of moving objects in dynamic image sequences has been carried through in this dissertation. Main research works and achievements of this dissertation are listed as follows:(1) A method of detecting moving point objects in dynamic image sequences is proposed, which is based on the combination of two methods: detecting method based on first-order norm of frame difference vectors and the method based on peak value detection in airspace searched by double windows. It can detect not only the point object whose displacement is no less than one pixel between two continuous frames, but also the point object whose displacement is no more than one pixel between two continuous frames and whose displacement gathered of multiple continuous frames is larger than one pixel. The result indicates that, this method provides a new solution to detect multiple moving point objects in dynamic image sequences effectively.(2) A method is proposed to estimate global motion, which is based on 6-parameter affine model. The method is to estimate the law of the camera motion, which causes the background moving in the image sequences. By the estimation of global motion, the pixel correspondences between adjacent frames can be attained, and frames can be compensated with the parameters get by the global motion estimation, thus the problem with complex moving background can be simplified to the one with static background. This method has a fine effect in the detection of moving objects (such as moving cars, moving people etc) in dynamic image sequences. (3) The block matching algorithm used in the global motion estimation is optimized, which reduces the calculate amount, increase the speed of program running, and increase the reliability of moving objects detection to a great extent.