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Non-member Although the correlation filtering tracker for visual target tracking has achieved excellent results in both accuracy and robustness, there are still some problems yet to be solved. Obtaining stable scale estimation using traditional trackers is a challenging problem in visual target tracking, and many trackers fail to handle scale change in complex video sequences. In order to solve the problems of scale change, partial occlusion and geometric deformation for target tracking effectively, a new tracker based on kernel correlation filtering is developed in our study. The tracker obtained with maximum posterior probability method has scale adaptive ability and can deal with scale change to improve the tracking ability. In addition, the tracker further enhances the ability to deal with illumination variation, geometric deformation and occlusion by fusing the adaptive color naming feature and the histogram of oriented gradient feature as well. The VOT-2018 which has 50 video sequences is used as the benchmark data set in this work and the simulation evaluation on this data set have shown that the proposed tracker has achieved stable tracking results in some challenging scenarios and can achieve better tracking performance than other trackers.

The structured output tracking algorithm is a visual target tracking algorithm with excellent comprehensive performance in recent years. However, the algorithm classifier will produce error information and result in target loss or tracking failure when the target is occluded or the scale changes in the process of tracking. In this work, a real-time structured output tracker with scale adaption is proposed: (1) the target position prediction is added in the process of target tracking to improve the real-time tracking performance; (2) the adaptive scheme of target scale discrimination is proposed in the structured support to improve the overall tracking accuracy; and (3) the Kalman filter is used to solve the occlusion problem of continuous tracking. Extensive evaluations on the OTB-2015 benchmark dataset with 100 sequences have shown that the proposed tracking algorithm can run at a highly efficient speed of 84 fps and perform favorably against other tracking algorithms.

Context-aware correlation filter tracker is one of the most advanced target trackers, and it has significant improvement in tracking accuracy and success rate compared with traditional trackers. However, because the complexity of background in the process of tracking can lead to inaccurate output response of target tracking, an accurate tracking model is difficult to be established. Moreover, the drift problem is easy to occur during the tracking process due to the imprecise tracking model, especially when the target has large area occlusion, fast motion, and deformation. Aiming at the drift problem in the target tracking process, a novel algorithm is proposed in this paper. The developed method derives the specific representation of constraint output by assuming that the output response is Gaussian distribution, and a variable update parameter is obtained based on the output constraint relationship at first, then the tracking filter is selectively updated with changeable update parameters and fixed update parameters, and finally, the target scale is updated with maximizing posterior probability distribution. The effectiveness of developed algorithm is verified by comparing with other trackers on OTB-50 and OTB-100 evaluation benchmark datasets, and the experimental results have shown that the suggested tracker has higher overall object tracking performance than other trackers.