KCF tracker – parallel and PREM implementations

The goal of this project is modify KCF tracker for use in the HERCULES project, where it will run on NVIDIA TX2 board. To achieve the needed performance we try various ways of parallelization of the algorithm including execution on the GPU. The aim is also to modify the code according to the PRedictable Execution Model (PREM).

Stable version of the tracker is available from a CTU server, development happens at GitHub here and here.

Prerequisites

The code depends on OpenCV 2.4 library and CMake (optionally with Ninja) is used for building. Depending on the version to be compiled you need to have development packages for FFTW, CUDA or OpenMP installed.

On TX2, the following command should install what's needed:

$ apt install cmake ninja-build libopencv-dev libfftw3-dev

Compilation

There are multiple ways how to compile the code.

Compile all supported versions

$ git submodule update --init
$ make -k

This will create several build-* directories and compile different versions in them. If prerequisites of some builds are missing, the -k option ensures that the errors are ignored. This uses Ninja build system, which is useful when building naively on TX2, because builds with ninja are faster (better parallelized) than with make.

To build only a specific version run make <version>. For example, CUDA-based version can be compiled with:

$ make cufft

Using cmake gui

$ git submodule update --init
$ mkdir build
$ cmake-gui .

• Use the just created build directory as "Where to build the binaries".
• Press "Configure".
• Choose desired build options. Each option has a comment briefly explaining what it does.
• Press "Generate" and close the window.

$ make -C build

Command line

$ git submodule update --init
$ mkdir build
$ cd build
$ cmake [options] ..  # see the tables below
$ make

The cmake options below allow to select, which version to build.

The following table shows how to configure different FFT implementations.

Option	Description
-DFFT=OpenCV	Use OpenCV to calculate FFT.
-DFFT=fftw	Use fftw and its plan_many and "New-array execute" functions. If std::async, OpenMP or cuFFTW is not used the plans will use 2 threads by default.
-DFFT=cuFFTW	Use cuFFTW interface to cuFFT library.
-DFFT=cuFFT	Use cuFFT. This version also uses pure CUDA implementation of ComplexMat class and Gaussian correlation.

With all of these FFT version additional options can be added:

Option	Description
-DASYNC=ON	Use C++ std::async to run computations for different scales in parallel. This doesn't work with BIG_BATCH mode.
-DBIG_BATCH=ON	Concatenate matrices of different scales to one big matrix and perform all computations on this matrix. This mode doesn't work with OpenCV FFT.
-DOPENMP=ON	Parallelize certain operation with OpenMP. This can only be used with OpenCV or fftw FFT implementations. By default it runs computations for differenct scales in parallel. With -DBIG_BATCH=ON it parallelizes the feature extraction and the search for maximal response for differenct scales. With fftw, Ffftw's plans will execute in parallel.
-DCUDA_DEBUG=ON	Adds calls cudaDeviceSynchronize after every CUDA function and kernel call.
-DOpenCV_DIR=/opt/opencv-3.3/share/OpenCV	Compile against a custom OpenCV version.

Compilation for non-TX2 CUDA

The CuFFT version is set up to run on NVIDIA Jetson TX2. If you want to run it on different architecture, change the --gpu-architecture sm_62 NVCC flag in /src/CMakeLists.txt to your architecture of NVIDIA GPU. To find what SM variation you architecture has look here.

Running

No matter which method is used to compile the code, the results will be kcf_vot binary.

It operates on an image sequence created according to VOT 2014 methodology. You can find some image sequences in vot2016 datatset.

The binary can be run as follows:

1. ./kcf_vot [options]

   The program looks for groundtruth.txt or region.txt and images.txt files in current directory.

   • images.txt contains a list of images to process, each on a separate line.
   • groundtruth.txt contains the correct location of the tracked object in each image as four corner points listed clockwise starting from bottom left corner. Only the first line from this file is used.
   • region.txt is an alternative way of specifying the location of the object to track via its bounding box (top_left_x, top_left_y, width, height) in the first frame.

2. ./kcf_vot [options] <directory>

   Looks for groundtruth.txt or region.txt and images.txt files in the given directory.

3. ./kcf_vot [options] <path/to/region.txt or groundtruth.txt> <path/to/images.txt> [path/to/output.txt]

By default the program generates file output.txt containing the bounding boxes of the tracked object in the format "top_left_x, top_left_y, width, height".

Options

Options	Description
--visualize, -v[delay_ms]	Visualize the output, optionally with specified delay. If the delay is 0 the program will wait for a key press.
--output, -o <output.txt>	Specify name of output file.
--debug, -d	Generate debug output.
--fit, -f[W[xH]]	Specifies the dimension to which the extracted patch should be scaled. It should be divisible by 4. No dimension is the same as 128x128, a single dimension W will result in patch size of W×W.

Authors

• Vít Karafiát, Michal Sojka

Original C++ implementation of KCF tracker was written by Tomas Vojir here and is reimplementation of algorithm presented in "High-Speed Tracking with Kernelized Correlation Filters" paper [1].

References

[1] João F. Henriques, Rui Caseiro, Pedro Martins, Jorge Batista, “High-Speed Tracking with Kernelized Correlation Filters“, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015

License

Copyright (c) 2014, Tomáš Vojíř
Copyright (c) 2018, Vít Karafiát
Copyright (c) 2018, Michal Sojka

Permission to use, copy, modify, and distribute this software for research purposes is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.