Object detection is a process of finding real-world objects such as faces, vehicles or pedestrians in images and videos. WBD is a master's thesis project on BUT FIT researching the topic of GPU accelerated object detection. The goal is to detect such objects, exploiting the capabilities of NVIDIA GPUs to their maximum and use them for other applications such as tracking, recognition or analysis.
- A binary detector with multiple detection options (both gpu and cpu)
- Source code + CMake build system
- Lots of documentation, such as the thesis itself
WaldboostDetector is only available as a binary built from the source code, but it's all pretty simple.
- Download and install CMake if you haven't done so already.
- Clone the repository using the following command:
$ git clone https://github.com/mmaci/vutbr-fit-waldboost-detector- For CMake, the source code is found in the src folder. Binaries can be built wherever you want.
- Following dependencies are needed: CUDA 5.0+, OpenCV 2.4.9+
- -D --dataset [filename] Input textfile containing dataset filenames.
- -V --video [filename] Input video.
- -c --csv CSV output.
- -v --verbose Verbose output.
- -t --timer Measure detection time.
- -o --visualoutput Shows a visual output.
- -s --survivors Outputs surviving threads every stage of the classifier.
- -d --visualdebug Shows a visual output including pyramidal and preprocessed image.
- -b --blocksize [8/16/32] Sets block size. Should be set to 8, 16 or 32 for (64, 256 or 1024 threads)
- -l --limitframes [number] Process only a given number of frames.
- -m --detectionmode [global/shared/prefixsum/hybrid/cpu] Sets the specific implementation to use.
The whole project is currently under construction. The implementation itself is mostly finished for the purpose of the thesis and I'm currently working on performance measurements. If you are interested in this topic, would like to contribute or have anything else on mind, you can contact me at macenauer.p@gmail.com.