Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately from feature extraction, and update these filters using a moving average operation with an empirical weight. These DCF trackers hardly benefit from the end-to-end training. In this paper, we propose the CREST algorithm to reformulate DCFs as a one-layer convolutional neural network. Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training. To reduce model degradation during online update, we apply residual learning to take appearance changes into account. Extensive experiments on the benchmark datasets demonstrate that our CREST tracker performs favorably against state-of-the-art trackers.
|Title of host publication||Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|Publication status||Published - 2017 Dec 22|
|Event||16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy|
Duration: 2017 Oct 22 → 2017 Oct 29
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Other||16th IEEE International Conference on Computer Vision, ICCV 2017|
|Period||17/10/22 → 17/10/29|
Bibliographical noteFunding Information:
This work is supported in part by the NSF CAREER Grant #1149783, gifts from Adobe and Nvidia.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition