CompleMat: Having just one create() method is sufficient

[hercules2020/kcf.git] / README.md

diff --git a/README.md b/README.md
index 61fe9b07e29e0c095b9aa9278ec954df1ecfdedb..856a3ead71ef61bfad88bf3dfc73c53aaad4c929 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 additional CUDA error checking, which calls cudaDeviceSynchronize after every CUDA function and kernel call.|
+| `-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. |
 
 
@@ -164,7 +165,7 @@ top_left_y, width, height".
 | --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`. |
+| --fit, -f[W[xH]] | Specifies the dimension to which the extracted patch should be scaled. It should be divisible by 4. No dimension or zero rounds the dimensions to the nearest smaller power of 2, a single dimension `W` will result in patch size of `W`×`W`. |
 
 
 ## Authors
@@ -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