Motion Analysis In Video Images

Motion analysis in video images is a hot topic among many science domain including Computer Science, Computer Vision and Artificial Intelligence. By extending the analysis from single, static image to moving images, motion analysis can obtain more useful information than static image, like the feature of the motion and the moving objects recognition. Motion analysis has a wide range of application perspective, including military, security, and video data compression. The whole procedure of motion analysis includes four steps: motion extraction, object recognition, motion tracking and motion analysis. The four steps can be regarded as "black box" or "filter", which transforms the data from a low-level form to a high-level, human readable form. Not all of the four steps need be available in all kinds of application; it's up to the context the application applies.This thesis focuses on the motion extraction and motion tracking. The morion extraction is the procedure extracting moving object from sequential images, which has the high importance because the following steps mainly depend on its result. Meanwhile, the complexity of the real world makes it a very complicated task, nowadays approaches partly depend on the assumption and restriction with certain level, none of them can be applicable in all contexts. The thesis firstly comprehensively goes over the popular moving extraction approaches, and then extends the basic "background difference" approach to improve its robustness. Before the motion tracking, the thesis pre-processes the result obtained from the moving extraction procedure, which reduces the redundant computation while increases the precision. During the motion tracking step, a non-parametric tracking algorithm, Camshift, is applied in real-time tracking. The Camshift algorithm uses the object's color feature doing robust and real-time tracking.In order to test the algorithms mentioned in this thesis, a small-scaled motion analysis system is built under the Windows platform, which has the following features:(1) Real-time video capture and display; (2) Real-time automatic moving object detection; (3) Real-time automatic moving object tracking.We use this system doing a lot of experiments under various contexts, and the experiments results are well examined. The results demonstrate that the system can do automatic motion detection and tracking with high real-time performance and moderate robustness. How to improve the robustness of the system is the task of future research.