C++ implementation of SORT: Simple, online, and real-time tracking of multiple objects in a video sequence.
Kuhn-Munkres (Hungarian) Algorithm in C++ is forked from: https://github.com/saebyn/munkres-cpp
- Ubuntu 16.04
- Docker 18.09.4
- OpenCV 3.4.2
- Boost 1.58.0
All of the 3rd party libraries are included in the provided docker image
- Open Dockerfile, change line #19 ARG USERNAME to your host user name (echo $USER)
- Open a terminal and run:
$ cd /path/to/sort-cpp $ docker build -t sort . $ ./docker_run.sh
To run the tracker with the provided detections and visualize the results:
- Download the 2D MOT 2015 benchmark dataset
- Create a symbolic link to the dataset
$ ln -s /path/to/MOT2015_challenge/data/2DMOT2015 /path/to/sort-cpp/mot_benchmark
- Run the demo
$ cd /path/to/sort-cpp $ mkdir build && cd "$_" $ cmake .. && make $ cd /path/to/sort-cpp/bin # Without display $ ./sort-cpp # With display $ ./sort-cpp -d
Using the Python implementation of metrics for benchmarking multiple object trackers (MOT) to evaluate metrics.
Layout for ground truth data
<GT_ROOT>/<SEQUENCE_1>/gt/gt.txt
<GT_ROOT>/<SEQUENCE_2>/gt/gt.txt
...
Layout for test data
<TEST_ROOT>/<SEQUENCE_1>.txt
<TEST_ROOT>/<SEQUENCE_2>.txt
...
Example:
mot_benchmark
├── test
│ ├── ADL-Rundle-6.txt
│ └── ADL-Rundle-8.txt
└── train
├── ADL-Rundle-6
│ └── gt
│ └── gt.txt
└── ADL-Rundle-8
└── gt
└── gt.txt
Sequences of ground truth and test will be matched according to the `<SEQUENCE_X>`
string.
# Optional for virtualenv
$ source ~/env/bin/activate
$ pip install motmetrics
# Usage
$ python -m motmetrics.apps.eval_motchallenge --help
# Format: python -m motmetrics.apps.eval_motchallenge groundtruths tests
$ python -m motmetrics.apps.eval_motchallenge mot_benchmark/train output/
FPS is around 1900 with Debug build.