文档介绍：Abstract
The vision-based object tracking has been an important and challenging research focus puter vision. Visual object tracking algorithms attract more and more attention, and it has a very wide applications such as intelligent transportation, smart video surveillance and robots. Although visual tracking has been studied for more than 30 years and many algorithms were proposed, it is still a challenge to design a appropriate visual object tracking system which is real-time, robust, precise and stable.
plex scenes, especially when the resolution between the target and background is low, the appearance model plays a key role in the identification of the object and the background. During tracking process, it is very important to handle object appearance variations, which contain both the intrinsic variations(such as pose changes and shape deformation) and extrinsic variations(such as illumination and occlusion). To handle these variations, a good appearance model is desired to meet two requirements: adaptivity that adapts to the intrinsic appearance variations and robustness that is invariant to the extrinsic appearance variations. plexity is very high in many tracking object searching algorithm, and most subsequent high-level applications(such as action recognition and retrieval) have harsh requirement in time, so how to reduce the time-consuming in object searching es a very important issue. In addition, the object searching process is closely related to real-time appearance model, it is also necessary to master appearance variations in object searching. To deal with two issues mentioned above, this thesis carries on in-depth study that including:
To solve the time-consuming problem and the low efficiency of the global exhaustive searching in the object tracking, this thesis proposed a new search strategy based on motion estimation and structural constraints. First, the motion vector of one object was calculated, associating with the location of the object in