Study On Occlusion Target Tracking In Intelligence Video Surveillance

In recent years, the technology of intelligence video surveillance is attached more importance to researches in computer vision. But in this technology there are many difficulties which include occlusion target tracking. In video surveillance which uses single camera, target occlusion is a common phenomenon due to camera views. The occlusion among targets causes great disturbances for accurately tracking occlusion targets and even for the application of video surveillance.In this paper, Bayesian theory is used for modeling target tracking and the tracking model is represented as different tracking types (target entrance, target exit, single target tracking and occlusion target tracking).In multiple target tracking, the tracking difficulties are difference according to different target situations. In order to measure tracking difficulties, intrackability theory is proposed and three kinds of concepts (intrackability of the whole sequence, intrackability of adjacent frames and intrackability of single target in adjacent frames) are described in this paper. The simplicity of intrackability computation is also proposed. It is confirmed by experiments that four factors (target number in the scent, target resolution, target velocity and distinctiveness of tracking features) affect intrackability. And two approaches (automatic target combination and dynamic feature cascade in occlusion target tracking) of tracking occlusion targets are proposed according to intrackability theory.Due to the limitation of occlusion target tracking approach which is introduced ahead, another general framework of tracking occlusion target is proposed. In this framework, the theory of occlusion layers is introduced to represent target occlusion partial order which is determined using the appearance features and velocity features in target overlapping patches. Then the target non-occlusion parts can be obtained according to the target occlusion relation and target states. Occlusion target is described by the combination of appearance features and velocity features. The traditional mean shift tracking algorithm is improved to estimate target position using appearance histograms and velocity histograms. The probabilities of all the target scale changes at current time are predicted according to target scales changes at previous time.In this paper, Markov Chain Monte Carlo approach is used for estimating optimal states of occlusion targets, because three types of parameters (target position, target scale and target occlusion relations) which describe target states are correspondence with each other; the parameter space which includes discrete variables and continuous variables varies with the number of occlusion targets. During the process of estimating the optimal target states, in order to accelerate the algorithm converges, the state transaction functions are established for these parameters which include target positions; occlusion relations and target scales. In order to obtain the optimal states, these parameters are gradually adjusted one by one using sampling approach. When the tracking algorithm converges, the model state is optimal for occlusion target states.This tracking algorithm is test by several image sequences with different kinds of occlusion targets. From the tracking results, we can see that this tracking algorithm can well track occlusion targets.