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

1. Open Dockerfile, change line #19 ARG USERNAME to your host user name (echo $USER)
2. 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:

1. Download the 2D MOT 2015 benchmark dataset
2. Create a symbolic link to the dataset

   $ ln -s /path/to/MOT2015_challenge/data/2DMOT2015 /path/to/sort-cpp/mot_benchmark

3. 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.

1. https://github.com/abewley/sort
2. https://github.com/mcximing/sort-cpp
3. https://github.com/saebyn/munkres-cpp