Robust object tracking via adaptive weight convolutional features

To solve the tracking failure problem in some videos caused by traditional deep learning tracking algorithms with fixed weight convolutional features, this paper proposes a novel tracking method combing the response map and the entropy function which considers the performance of each layer of convolutional neural networks and automatically adjusts the weight parameters. At the same time, an EdgeBoxes detection scheme is introduced when the maximum value of tracking response is less than a given threshold. A great number of bounding boxes are extracted by a sliding window and are evaluated by the EdgeBoxes detection scheme which generates the original proposal bounding boxes. Finally, the tracking method based on the correlation filter are conducted on the original proposal bounding boxes with the update scheme given. We have tested the proposed algorithm and nine state-of-the-art approaches on OTB-2013 video databases. Experimental results demonstrate that the proposed method has a higher precision and overlap rate. © 2019, The Editorial Board of Journal of Xidian University. All right reserved.