Research On Tracking Algorithm For Moving Vehicles Under Complex Environments

As a key technology of intelligent transportation system, moving vehicle tracking has become a vibrant research topic in the field of computer vision research. So how to how to take the advantage of moving vehicle tracking technology and improve the performance of moving vehicle tracking as much as possible is gradually becoming a research hotspot in recent years. However, the research of this field is still in its early stage and many problems remain unsolved. For one thing, the application of moving vehicle tracking to traffic surveillance often requires real-time process, which restricts the complexity of the algorithms. For another, the diversity and complicacy of the traffic scenes require the system to take many different conditions into account. Some existing algorithms seem to be too simple to handle the complex situation. Therefore, how to track the moving vehicles steady has become a more meaningful and challenging research topic under the complex background.For the difficulties of moving vehicle tracking under the complex background, in this paper, several classical tracking algorithm for moving object are summarized and profoundly analyzed, which are mainly feature-based or snake-based or active contour-based or particle-based , and makes effective improvement. The following is done in this thesis:1.Comprehensive analysis of the standard particle filter and geometric active depends on parameters selection, can not handle changes in curve topology. In viewing this, a new particle filter target tracking algorithm based on geometric active contours was proposed, it combine the advantages of particle filter and geometric active contour models, handled changes of curve topology with level set, and improve the re-sampling techniques, and increase the reliability of observation whith describing the target.2.For the instability of moving vehicle track by using a single measurement source under the complex background, a new particle filter algorithm was proposed which combined the moving feature with contour information. The methord used moving likelihood model to predict object, geometric active contour model to update the particle, and Stratified Re-sampling method to overcome the particle degradation.Simulation results show that the proposed tracking algorithm is feasible. The first one can handle changes of curve topology and effectively improve the state estimation precision with more flexibility. The secend algorithm can deal with the target in occlusion and in turn effectively.