Search

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. [ABSTRACT FROM AUTHOR]

In recent years, correlation filters have achieved excellent performance in terms of accuracy and robustness for visual target tracking. However, it is still a challenging task to improve the tracking capability which can handle complex background and deal with target scale changes. In this paper, a new tracker based on the correlation filter framework with scale adaptation for visual target tracking is presented: 1) an effective scale adaptive scheme, which can solve the fixed template size with the basis of traditional correlation filter tracker, is proposed; 2) two powerful features including histogram of oriented gradient and color name, which can improve the overall tracking performance, are integrated together; 3) a high-confidence model updating strategy, which can avoid the model occlusion problem during model updating, is developed. The extensive experimental evaluations on OTB-2013 dataset have demonstrated that the developed novel tracker in our study can successfully outperform the other trackers on the benchmark dataset with 51 sequences. It is very promising for various challenging scenarios. [ABSTRACT FROM AUTHOR]