Research Of Moving Object Tracking Based On Video Sequence

Target tracking plays an important role in areas such as human-computer interaction, intelligent transportation, video monitoring, but we should do some in-depth research of the technology because of the complicated scene. It will bring disturbance because of the target’s posture variation, illumination change, object occlusion and high speed movement, so building a robust tracking system is our research direction.This paper mainly studies the target tracking algorithm with the system framework of TLD(Tracking Learning Detection). TLD algorithm could achieve long-term online target tracking, which is mainly composed of three parts: the tracker, learning module, the detector. This paper selects three scene with different complexity to simulate the TLD tracking algorithms, results show that the track can be rediscovered after lose, and the algorithms has a certain robustness. However, TLD tracking algorithms is sensitive to light and posture variation, long time to rediscover tracker after missed and so on when in occlusion, occlusion or posture variation scene. A TLD model and Mean Shift based tracking algorithms, a refine tracking object can be got by dynamically setting the retrieved initial point of Mean Shift with the degree of TLD confidence. Similarly, the new tracking algorithm is simulated in three scenes.The experimental results show that the improved TLD target tracking algorithm can track the target effectively when posture variation and illumination change. The target will be captured quickly when reappear. The localization error of bounding box is greatly decreased, and the overlap degree is greatly increased. So the improved TLD target tracking algorithm can effectively adapt to the complexity of the environment object tracking, at the same time be able to the drift of the target and the loss of the target,it makes the tracking system more robust, accurate and real-time.