Refactor p_pose and related variables

[hercules2020/kcf.git] / README.md

diff --git a/README.md b/README.md
index 2e9f412ac7623739ad4150e76b37aa29b72dd535..8cf2c6388337a4cdbdbf481e84e20672400e7e48 100644 (file)
--- a/README.md
+++ b/README.md
@@ -6,11 +6,12 @@ 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 [CTU server][2],
-development happens at [GitHub][3].
+Stable version of the tracker is available from a [CTU server][2],
+development happens at GitHub [here][wsh] and [here][3].
 
 [1]: http://hercules2020.eu/
 [2]: http://rtime.felk.cvut.cz/gitweb/hercules2020/kcf.git
+[wsh]: https://github.com/wentasah/kcf
 [3]: https://github.com/Shanigen/kcf
 
 ## Prerequisites
@@ -103,7 +104,7 @@ With all of these FFT version additional options can be added:
 | `-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` | This mode adds CUDA error checking for all kernels and CUDA runtime libraries. Only works with `cuFFT` version.|
+| `-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. |
 
 
@@ -172,13 +173,13 @@ top_left_y, width, height".
 
 Original C++ implementation of KCF tracker was written by Tomas Vojir
 [here][12] and is reimplementation of algorithm presented in
-"High-Speed Tracking with Kernelized Correlation Filters" paper [1].
+"High-Speed Tracking with Kernelized Correlation Filters" paper \[1].
 
 [12]: https://github.com/vojirt/kcf/blob/master/README.md
 
 ## References
 
-[1] João F. Henriques, Rui Caseiro, Pedro Martins, Jorge Batista,
+\[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