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data/Jogging1
utility
.gitattributes
README.md
choose_video.m
download_videos.asv
download_videos.m
external.txt
fhog.m
gaussian_correlation.m
gaussian_shaped_labels.m
get_feature_map.m
get_features.m
get_subwindow.m
gradientMex.cpp
gradientMex.mexa64
gradientMex.mexw64
im2c.m
linear_correlation.m
load_video_info.m
polynomial_correlation.m
precision_plot.m
run_samf.m
run_tracker.m
show_video.m
tracker.m
videofig.m
w2crs.mat

README.md

A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration (SAMF)

This is the matlab code of SAMF[1]. It won the second place in VOT 2014. The implementation is built upon the code of [2]. The codes provided by [3,4,5] are also used in the implementation.

Instructions:

    1. Modify the base_path in "run_tracker.m" with your own setting.
    1. Run the "run_tracker.m" script in MATLAB.
    1. Choose sequence.

Contact:

Yang Li, liyang89@zju.edu.cn ihpdep.github.io

Jianke Zhu jkzhu@zju.edu.cn jkzhu.github.io

Our Lab website

Reference

[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.

Acknowledge:

Many thanks to Guy Koren and Jifeng Ning(宁纪锋) for helping me to find and fix bugs!