YoloXtrack.mp4
The model tries to keep track of the individual objects found in the scenes. The example video follows the walkers as they stroll along. Obvious, not a simple task. Not only does the detection needs to be at a robust level. You also have to solve occlusion as the walkers pass each other.
Due to the excellent recognition, the best tracker in the set.
Paper: https://arxiv.org/pdf/2110.06864v2.pdf
Special made for a bare Raspberry Pi 4, see Q-engineering deep learning examples
| Model | size | objects | mAP | RPi 4 64-OS 1950 MHz |
|---|---|---|---|---|
| Tensorflow lite | 300x300 | 80 | 21.0 | 24.6 FPS |
| NanoDet | 320x320 | 80 | 20.6 | 11.8 FPS |
| YoloX | 416x416 | 80 | 25.8 | 7.2 FPS |
To run the application, you have to:
- A raspberry Pi 4 with a 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. Install 64-bit OS
- The Tencent ncnn framework installed. Install ncnn
- OpenCV 64 bit installed. Install OpenCV 4.5
- Code::Blocks installed. (
$ sudo apt-get install codeblocks)
To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/YoloX-Tracking-ncnn-RPi_64-bit/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE and README.md as they are no longer needed.
$ rm master.zip
$ rm LICENSE
$ rm README.md
Your MyDir folder must now look like this:
├── include
│ ├── BYTETracker.h
│ ├── dataType.h
│ ├── kalmanFilter.h
│ ├── lapjv.h
│ └── STrack.h
├── palace.mp4
├── src
│ ├── BYTETracker.cpp
│ ├── kalmanFilter.cpp
│ ├── lapjv.cpp
│ ├── STrack.cpp
│ ├── utils.cpp
│ └── yoloX.cpp
├── tree.txt
├── Walkers.mp4
├── YoloX_Byte_Track.cbp
├── yoloxN.bin
└── yoloxN.param
Install eigen-3.3.9 google or baidu(code:ueq4) (if not done earlier by Tensorflow Lite).
unzip eigen-3.3.9.zip
cd eigen-3.3.9
mkdir build
cd build
cmake ..
sudo make install
To run the application load the project file YoloX_Byte_Track.cbp in Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at Hands-On.
Many thanks to nihui and ifzhang
Instead of Code::Blocks, you can also use CMake to build the application.
Please follow the next instructions. Assuming your in the 'main' directory.
.
├── bin
├── build
├── CMakeLists.txt
├── include
├── obj
├── palace.mp4
├── src
├── tree.txt
├── Walkers.mp4
├── YoloX_Byte_Track.cbp
├── YoloX_Byte_Track.depend
├── YoloX_Byte_Track.layout
├── yoloxN.bin
└── yoloxN.param
Create a build folder
$ mkdir build
$ cd build
Run CMake and Make
$ cmake ..
-- The C compiler identification is GNU 8.3.0
-- The CXX compiler identification is GNU 8.3.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found OpenCV: /usr/local (found version "4.5.1")
-- Configuring done
-- Generating done
-- Build files have been written to: /home/pi/software/YoloX ByteTrack/build
$ make -j4
Scanning dependencies of target YoloX_Track
[ 28%] Building CXX object CMakeFiles/YoloX_Track.dir/src/BYTETracker.cpp.o
[ 28%] Building CXX object CMakeFiles/YoloX_Track.dir/src/STrack.cpp.o
[ 57%] Building CXX object CMakeFiles/YoloX_Track.dir/src/kalmanFilter.cpp.o
[ 57%] Building CXX object CMakeFiles/YoloX_Track.dir/src/lapjv.cpp.o
[ 71%] Building CXX object CMakeFiles/YoloX_Track.dir/src/utils.cpp.o
[ 85%] Building CXX object CMakeFiles/YoloX_Track.dir/src/yoloX.cpp.o
[100%] Linking CXX executable ../YoloX_Track
[100%] Built target YoloX_Track
$ cd ..
$ tree -L 1
.
├── bin
├── build
├── CMakeLists.txt
├── include
├── obj
├── palace.mp4
├── src
├── tree.txt
├── Walkers.mp4
├── YoloX_Byte_Track.cbp
├── YoloX_Byte_Track.depend
├── YoloX_Byte_Track.layout
├── yoloxN.bin
├── yoloxN.param
└── YoloX_Track
Run
$ ./YoloX_Track
Once YoloX_Track works, you may remove the build directory, since we don't need it any more.
$ sudo rm -rf build
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