"Target Response Adaptation for Correlation Filter Tracking" ECCV2016 Authors: Adel Bibi, Matthias Mueller, and Bernard Ghanem. Visit our group's website: https://ivul.kaust.edu.sa/Pages/Home.aspx Adel Bibi's website: www.adelbibi.com Email: adel.bibi [AT] kaust.edu.sa bibiadel93 [AT] gmail.com matthias.mueller.2 [AT] kaust.edu.sa Bernard.Ghanem [AT] kaust.edu.sa Website: Adel Bibi: www.adelbibi.com Bernard Ghanem: http://www.bernardghanem.com/ IVUL: https://ivul.kaust.edu.sa/Pages/Home.aspx Please cite: @inproceedings{bibi2016target, title={Target response adaptation for correlation filter tracking}, author={Bibi, Adel and Mueller, Matthias and Ghanem, Bernard}, booktitle={European Conference on Computer Vision}, pages={419--433}, year={2016}, organization={Springer} } ************************************************** This is a MATLAB implementation on the adaptive target for correlation filters. The framework is generic and can be directly implemented in any correlation tracker that solves the following objective. ||Ax - b||_2^2 + \lambda ||x||_2^2. * The code is based on the tracker SAMF [1]. It is free for research use. If you find it useful, please acknowledge the paper above with a reference. ************************************************** The code is integratable with the OTB100 and OTB50 evaulation benchmarks. To run the code over the complete benchmark: 1- Move the complete traker directory to the "Trackers" directory in the OTB evulation code. Locate the function "configTrackers.m" in the OTB100 evaulation code. To install the OTB100 benchmark: [1] http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html [2] https://sites.google.com/site/trackerbenchmark/benchmarks/v10 2- Add the following line to the list of trackers to be evualted over: struct('name','SAMF_AT','namePaper','SAMF_AT') Note: The code that will be run through the evaulation by running the function "run_SAMF_AT.m". ************************************************** Referrences: [1] A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. European Conference on Computer Vision Workshops 2014. [2] Henriques, João F., et al. "High-speed tracking with kernelized correlation filters." Pattern Analysis and Machine Intelligence, IEEE Transactions on 37.3 (2015): 583-596. [3] Henriques, Joao F., et al. "Exploiting the circulant structure of tracking-by-detection with kernels." Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 702-715. [4] Wu, Yi, Jongwoo Lim, and Ming-Hsuan Yang. "Online object tracking: A benchmark." Proceedings of the IEEE conference on computer vision and pattern recognition. 2013. A complete list of references can be found in the paper, which can be found here https://ivul.kaust.edu.sa/Pages/Pub-Adaptive-Kernelized-Correlation-Filters.aspx Paper: http://www.adelbibi.com/papers/ECCV2016/Target_Adap.pdf Supplemental Material: [1] http://www.adelbibi.com/papers/ECCV2016/Target_Adap_supp.pdf [2] https://www.youtube.com/watch?v=yZVY_Evxm3I