This is the matlab code of SAMF[1]. It won the second place in VOT 2014(http://votchallenge.net/vot2014/index.html). The implementation is built upon the code of [2]. The codes provided by [3,4,5] are also used in the implementation.
Instructions:
- Modify the base_path in "run_tracker.m" with your own setting.
- Run the "run_tracker.m" script in MATLAB.
- Choose sequence.
For CVPR2013 benchmark[]
Contact: Yang Li, liyang89@zju.edu.cn ihpdep.github.io
Jianke Zhu jkzhu@zju.edu.cn jkzhu.github.io
[1] Yang Li, Jianke Zhu. "A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration" European Conference on Computer Vision, Workshop VOT2014 (ECCVW), 2014
[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista. "High-Speed Tracking with Kernelized Correlation Filters." TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
[3] Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg and Joost van de Weijer. "Adaptive Color Attributes for Real-Time Visual Tracking". Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
[4] J. van de Weijer, C. Schmid, J. J. Verbeek, and D. Larlus. "Learning color names for real-world applications." TIP, 18(7):1512–1524, 2009.
[5] David Ross, Jongwoo Lim, Ruei-Sung Lin, Ming-Hsuan Yang. "Incremental Learning for Robust Visual Tracking" In the International Journal of Computer Vision, Special Issue: Learning for Vision, 2007.
[6] Y. Wu, J. Lim, M.-H. Yang, "Online Object Tracking: A Benchmark", CVPR 2013. Website: http://visual-tracking.net/