LADCF - No 1 Algorithm on the public dataset of VOT2018
Codes of 'Learning Adaptive Discriminative Correlation Filters (LADCF) via Temporal Consistency preserving Spatial Feature Selection for Robust Visual Tracking' for VOT2018
@article{xu2018learning, title={Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking}, author={Xu, Tianyang and Feng, Zhen-Hua and Wu, Xiao-Jun and Kittler, Josef}, journal={arXiv preprint arXiv:1807.11348}, year={2018}}
The tracker codes for the original paper can be download here.
Learning Adaptive Discriminative Correlation Filter on Low-dimensional Manifold (LADCF) utilises adaptive spatial regularizer to train low-dimensional discriminative correlation filters. We follow a single-frame learning and updating strategy: the filters are learned after tracking stage and then updated using a fixed rate [1]. We use HOG [2], CN [3], and ResNet-50 [4] as our features. For deep features, we augment the training data using blur (2 gaussian filters), rotation (-30, -20, -10, 10, 20, 30) and flip (horizontal) [5]. Code modules refer to ECO [6] in feature extraction.
Run install.m file to compile the libraries. Copy the tracker_LADCF.m to the vot-workspace. (replace #LOCATION with the path of this folder)
- MatConvNet
- PDollar Toolbox
- mtimesx (https://github.com/martin-danelljan/ECO/tree/master/external_libs/mtimesx)
- mexResize (https://github.com/martin-danelljan/ECO/tree/master/external_libs/mexResize)
Ubuntu 14.04 LTS, Matlab R2016a, CPU Intel(R) Xeon(R) E5-2643
- [1] Henriques, João F., et al. "High-speed tracking with kernelized correlation filters." IEEE Transactions on Pattern Analysis and Machine Intelligence 37.3 (2015): 583-596.
- [2] Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005.
- [3] Van De Weijer, Joost, et al. "Learning color names for real-world applications." IEEE Transactions on Image Processing 18.7 (2009): 1512-1523.
- [4] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- [5] Bhat, Goutam, Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, and Michael Felsberg. "Unveiling the Power of Deep Tracking." arXiv preprint arXiv:1804.06833 (2018).
- [6] Danelljan, Martin, et al. "Eco: Efficient convolution operators for tracking." Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.