The Research Of Anti-occlusion Object Tracking Algorithm Based On Visual-cue Fusion

Visual object tracking is a crucial problem in computer vision, and it has a widerange of applications in video surveillance, human-computer interaction, robot visionnavigation etc. A universal, general tracking algorithm have to deal with complexbackground, lighting, similar objects, occlusion, and other difficulties. Researchershave put forward a lot of tracking algorithms, but a universal robust tracking algorithmcontinues to be a challenging subject. Among them, occlusion problem is the keyfactor to limit the robustness of tracking algorithm. So this thesis is mainly to studyand discuss the occlusion problem in the object tracking, the main works are asfollows:1. For the occlusion problem in object tracking, in the framework of the particlefilter, two kinds of improved algorithms are proposed. First algorithm for the problemof partial occlusion or short full occlusion, the algorithm based on single-cue can nottrack the object effectively， an anti-occlusion object tracking algorithm based onmulti-cue adaptive fusion is proposed. When an occlusion occurs, switch integrationpolicy in time, enter to occlusion tracking mode, also embed mean-shift algorithm toovercome the degradation of particles. Experimental results show that the algorithmhas a strong anti-occlusion ability that is able to track objects under complexbackground; Second, to deal with severe occlusion or long full occlusion, meanwhile,for uncertainty of occlusion process, the occlusion object may continues to forward,backward or standing still, a further optimization based on the first algorithm isintroduced to make model of occlusion process more accurate, when the occlusionoccurs, change the object motion model at the same time, the particles only do theBrownian motion of random walk. Simulation results show that the algorithm isbalanced with robust and occlusion handling, which has a more general sense and cantrack objects in complex changing scene.2. To deal with occlusion problem in object tracking, within the framework ofMean-shift, two improved algorithms are introduced. The first algorithm for partialocclusion or short full occlusion, tracking robustness is not high using single-featurecues and the object is easy to lose when in occlusion, an anti-occlusion mean-shiftalgorithm based on multi-cue fusion is presented. when the occlusion occurs, usingKalman filter to estimate the object prediction state; a occlusion factor as objectocclusion criteria is introduced, under heavy occlusion,using extrapolated to predict the object location. Experimental results show that the algorithm has strongeranti-interference, and can still track the target correctly even in fully occlusion;second,to deal with severe occlusion or long full occlusion, meanwhile, for the motionrandomness after the object occlusion, the algorithm based on extrapolation of theKalman prediction may fail, and the processing of occlusion of the object using theparticle filter algorithm is more reasonable, so, fusing the advantages of bothalgorithms, when occlusion occurs, switch to the particle filter algorithm.Experimental results show that the algorithm has a good performance in robustness,real-time, can effectively solve the occlusion problem in the tracking process.At thesame time how to compare, evaluate various algorithms, there is still not a unifiedsimulation test platform. So, an anti-object tracking system simulation platform basedon Matlab GUI is given, in the end, a simulation example which compare the samesequence is given.