abewley/sort

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 this paper. At the time of the initial publication, SORT was ranked the best open source multiple object tracker on the MOT benchmark.

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:

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

0. 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).

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)
...

🔗 See also

• DeepSORT - The successor of SORT with a Deep Association Metric used injecting appearance information to improve the association in difficult scenarios such as occlusions and fast moving objects.
• Local Metrics for Multi-Object Tracking - A framework to help better measure and understand how well your tracker performs at association across time as opposed to the standard metrics (MOTA, HOTA) which as heavilty biased towards raw detection performance.
• Human Scene Transformer - A recent repository for trajectory forecasting, a future direction beyond tracking (pun intended).