人工智能挑战赛步兵车上层ROS源码+项目说明.zip

# C++ KCF Tracker This package includes a C++ class with several tracking methods based on the Kernelized Correlation Filter (KCF) [1, 2]. It also includes an executable to interface with the VOT benchmark. [1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, "High-Speed Tracking with Kernelized Correlation Filters", TPAMI 2015. [2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, "Exploiting the Circulant Structure of Tracking-by-detection with Kernels", ECCV 2012. Authors: Joao Faro, Christian Bailer, Joao F. Henriques Contacts: joaopfaro@gmail.com, Christian.Bailer@dfki.de, henriques@isr.uc.pt Institute of Systems and Robotics - University of Coimbra / Department of Augmented Vision DFKI ### Algorithms (in this folder) ### "KCFC++", command: ./KCF Description: KCF on HOG features, ported to C++ OpenCV. The original Matlab tracker placed 3rd in VOT 2014. "KCFLabC++", command: ./KCF lab Description: KCF on HOG and Lab features, ported to C++ OpenCV. The Lab features are computed by quantizing CIE-Lab colors into 15 centroids, obtained from natural images by k-means. The CSK tracker [2] is also implemented as a bonus, simply by using raw grayscale as features (the filter becomes single-channel). ### Compilation instructions ### There are no external dependencies other than OpenCV 3.0.0. Tested on a freshly installed Ubuntu 14.04. 1) cmake CMakeLists.txt 2) make ### Running instructions ### The runtracker.cpp is prepared to be used with the VOT toolkit. The executable "KCF" should be called as: ./KCF [OPTION_1] [OPTION_2] [...] Options available: gray - Use raw gray level features as in [1]. hog - Use HOG features as in [2]. lab - Use Lab colorspace features. This option will also enable HOG features by default. singlescale - Performs single-scale detection, using a variable-size window. fixed_window - Keep the window size fixed when in single-scale mode (multi-scale always used a fixed window). show - Show the results in a window. To include it in your project, without the VOT toolkit you just need to: // Create the KCFTracker object with one of the available options KCFTracker tracker(HOG, FIXEDWINDOW, MULTISCALE, LAB); // Give the first frame and the position of the object to the tracker tracker.init( Rect(xMin, yMin, width, height), frame ); // Get the position of the object for the new frame result = tracker.update(frame);