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SORT

A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences. See an example video here.

By Alex Bewley

Introduction

SORT is a barebones implementation of a visual multiple object tracking framework based on rudimentary data association and state estimation techniques. It is designed for online tracking applications where only past and current frames are available and the method produces object identities on the fly. While this minimalistic tracker doesn't handle occlusion or re-entering objects its purpose is to serve as a baseline and testbed for the development of future trackers.

SORT was initially described in an arXiv tech report. At the time of the initial publication, SORT was ranked the best open source multiple object tracker on the MOT benchmark.

This code has been tested on Mac OSX 10.10, and Ubuntu 14.04, with Python 2.7 (anaconda).

Note: A significant proportion of SORT's accuracy is attributed to the detections. For your convenience, this repo also contains Faster RCNN detections for the MOT benchmark sequences in the benchmark format. To run the detector yourself please see the original Faster RCNN project or the python reimplementation of py-faster-rcnn by Ross Girshick.

Also see: A new and improved version of SORT with a Deep Association Metric implemented in tensorflow is available at https://github.com/nwojke/deep_sort .

License

SORT is released under the GPL License (refer to the LICENSE file for details) to promote the open use of the tracker and future improvements. If you require a permissive license contact Alex ([email protected]).

Citing SORT

If you find this repo useful in your research, please consider citing:

@inproceedings{Bewley2016_sort,
  author={Bewley, Alex and Ge, Zongyuan and Ott, Lionel and Ramos, Fabio and Upcroft, Ben},
  booktitle={2016 IEEE International Conference on Image Processing (ICIP)},
  title={Simple online and realtime tracking},
  year={2016},
  pages={3464-3468},
  keywords={Benchmark testing;Complexity theory;Detectors;Kalman filters;Target tracking;Visualization;Computer Vision;Data Association;Detection;Multiple Object Tracking},
  doi={10.1109/ICIP.2016.7533003}
}

Dependencies:

This code makes use of the following packages:

  1. scikit-learn
  2. scikit-image
  3. FilterPy

To install required dependencies run:

$ pip install -r requirements.txt

Demo:

To run the tracker with the provided detections:

$ cd path/to/sort
$ python sort.py

To display the results you need to:

  1. Download the 2D MOT 2015 benchmark dataset
  2. Create a symbolic link to the dataset
$ ln -s /path/to/MOT2015_challenge/data/2DMOT2015 mot_benchmark
  1. Run the demo with the --display flag
$ python sort.py --display

Main Results

Using the MOT challenge devkit the method produces the following results (as described in the paper).

Sequence Rcll Prcn FAR GT MT PT ML FP FN IDs FM MOTA MOTP MOTAL
TUD-Campus 68.5 94.3 0.21 8 6 2 0 15 113 6 9 62.7 73.7 64.1
ETH-Sunnyday 77.5 81.9 0.90 30 11 16 3 319 418 22 54 59.1 74.4 60.3
ETH-Pedcross2 51.9 90.8 0.39 133 17 60 56 330 3014 77 103 45.4 74.8 46.6
ADL-Rundle-8 44.3 75.8 1.47 28 6 16 6 959 3781 103 211 28.6 71.1 30.1
Venice-2 42.5 64.8 2.75 26 7 9 10 1650 4109 57 106 18.6 73.4 19.3
KITTI-17 67.1 92.3 0.26 9 1 8 0 38 225 9 16 60.2 72.3 61.3
Overall 49.5 77.5 1.24 234 48 111 75 3311 11660 274 499 34.0 73.3 35.1

Using SORT in your own project

Below is the gist of how to instantiate and update SORT. See the 'main' section of sort.py for a complete example.

from sort import *

#create instance of SORT
mot_tracker = Sort() 

# get detections
...

# update SORT
track_bbs_ids = mot_tracker.update(detections)

# track_bbs_ids is a np array where each row contains a valid bounding box and track_id (last column)
...