Research On Visual Tracking Algorithm Based On Regional Features Compression And Fusion

Visual tracking is an important and challenging research direction in the field of machine vision, and it plays a key role in intelligent video surveillance, intelligent transportation, weather analysis, remote measurement and medical image analysis and so on.In rencent years, lots of tracking algorithms have been proposed by many research institutions and researchers, however majority of the proposed algorithms only perform well under certain conditions. The performance of target tracking algorithm can still be greatly improved. In the actual process of tracking, the appearance of the target and the environment around it are constantly changing. Therefore, exploring a tracking algorithm which has high robustness and high accuracy is still a difficult task.This paper based on the regional feature compression and feature fusion research and intended to construct a high accurate and robust target tracking algorithm. We explored some content of target appearance model of sub regional feature based, feature compression and fusion theory and flexible classifier construction.1. To solve the problem of illumination change, occlusion and appearance change and weak description ability of single feature, the proposed algorithm makes use of multiple features extracted from the sample sub-regions and target appearance model is established by multiple features. Multiple features can provide rich information of the target and the method of sub-region division of the sample can make the tracking algorithm effectively deal with the problem of occlusion and illumination change. Because the classifier of fixed statistical characteristics is very easy to introduce weak discriminative features, resulting in instablity of target appearance model and decrease of accuracy of tracking algorithm. So we use a weighted naive bias classifier with flexible statistical property to accomplish the target tracking task. compressive sensing theory is used to reduce the dimension of multi features of high dimension to improve the speed of the proposed algorithm.2. To solve the problem of complex background and target rotation in the tracking process, the proposed algorithm establishes target appearance model with perceptual hash features of the random haaar-like image blocks extracted from the sample sub-regions. In the classifier judgment part, our paper takes the search strategy of neareast search principle instead of greedy search method. Hamming distance is used as target match measurement and the judgment of classifier. This algorithm can effectively deal with the problem of background clutter, object appearance change and similar background. Experiments show that the algorithm has better accuracy and better robustness.3. We test our two algorithms on many standard video test sequences and make qualitative and quantitative analysis of our methods in our experiment. Compared with three representative popular tracking algorithms, which are mean shift target tracking(meanshift), real-time compressive tracking algorithm(CT) and fast compressive tracking algorithm(FCT), it showes that the first tracking algorithm speed is solwer than the CT algorithm slightly, but the accuracy is greatly improved. Especially in the scene of occlusion and lllumination changes, the algorithm performs well. The second algorithm has higher accuracy. Especially in dealing with the problem of target rotation and background clutter, the second algorithm is more robust.