/KCFDP

"Kernelized Correlation Filter with Detection Proposal" presented in "Enable Scale and Aspect Ratio Adaptability in Visual Tracking with Detection Proposals", BMVC, 2015.

Primary LanguageC++OtherNOASSERTION

This is the visual object tracker KCFDP presented in: [1] "Enable Scale and Aspect Ratio Adaptability in Visual Tracking with Detection Proposals", BMVC, 2015, Dafei Huang, Lei Luo, Mei Wen, Zhaoyun Chen and Chunyuan Zhang.

The implementation is built upon: Color-name feature integration and model updating scheme: [2] "Adaptive Color Attributes for Real-Time Visual Tracking", CVPR, 2014, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg and Joost van de Weijer. [3] "Learning color names for real-world applications", TIP, 18(7):1512-1524, 2009, J. van de Weijer, C. Schmid, J. J. Verbeek and D. Larlus.

Original CSK and KCF tracking framework: [4] "Exploiting the circulant structure of tracking-by-detection with kernels", ECCV, 2012, J. F. Henriques, R. Caseiro, P. Martins and J. Batista. [5] "High-Speed Tracking with Kernelized Correlation Filters", TPAMI, 2014, J. F. Henriques, R. Caseiro, P. Martins and J. Batista. http://www.isr.uc.pt/~henriques/circulant/

Structured Forests edge detector and Edge Boxes detection proposal generator: [6] "Structured Forests for Fast Edge Detection", ICCV, 2013, P. Dollar and C. Zitnick. [7] "Edge Boxes: Locating Object Proposals from Edges", ECCV, 2014, C. Zitnick and P. Dollar.

The IoU calculation code and example sequence along with annotations: [8] "Online Object Tracking: A Benchmark", CVPR, 2013, Y. Wu, J. Lim and M.-H. Yang. http://visual-tracking.net/

Additional tools needed when running the code: [9] "Piotr's Image and Video Matlab Toolbox (PMT)", P. Dollar. http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html

Codes above are integrated and modified by Dafei Huang.

Quick Start Guide: Running the code directly on Girl[8] sequence:

  1. Download and compile PMT[9];
  2. Modify the path in Line 59 of run_tracker.m to your PMT path;
  3. Go to the root directory of this code and run "run_tracker" in Matlab.

Integrating the code into OTB[8] tracking benchmark suite:

  1. Download and prepare your environment according to [8];
  2. Download and compile PMT[9];
  3. Modify the path in Line 58 of run_KCFDP.m to your PMT path;
  4. Copy the whole directory of this code into OTB_ROOT_PATH/tackers/;
  5. Add a new line "struct('name','KCFDP','namePaper','KCFDP'),..." into the "trackers1" array in OTB_ROOT_PATH/util/configTrackers.m;
  6. Run the OTB benchmark suite according to [8].

NOTE:

  1. For your convenience we have generated the binary files of [6] and [7] for 64-bit MAC OS, Windows, and Linux. Please recompile the codes in ./private/ if needed.
  2. The following files are part of Structured Forests[6] and Edge Boxes[7], and are provided for convenience only: edgeBoxes.m, edgesChns.m, edgesDetect.m, modelBsds.mat, edgeBoxesMex.cpp, edgesDetectMex.cpp, edgesNmsMex.cpp, spDetectMex.cpp and their relevant binary files. These files are under the license specified in license_Structured_Forests_and_Edge_Boxes.txt.
  3. The following files are part of ACT[2], and are provided for convenience only: im2c.m, get_feature_map.m, w2crs.mat. Please refer to readme_ACT.txt for the authorship information.
  4. The tracking framework utilized here is from KCF[5] under the license specified in license_KCF.txt.
  5. calcRectInt.m and the example sequence along with annotations are from OTB[8] under the GNU-GPL license.
  6. The rest parts of KCFDP are distributed under the BSD license:

Contact: Dafei Huang huangdafei1012@163.com