Method

Simple Online Realtime Tracking [SORT]


Submitted on 31 Jul. 2020 11:30 by
Issa Mouawad (University of Genoa)

Running time:.002 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
This paper explores a pragmatic approach to
multiple object tracking where the main focus is to
associate objects efficiently for online and
realtime applications. To this end, detection
quality is identified as a key factor influencing
tracking performance, where changing the detector
can improve tracking by up to 18.9%. Despite only
using a rudimentary combination of familiar
techniques such as the Kalman Filter and Hungarian
algorithm for the tracking components, this
approach achieves an accuracy comparable to state-
of-the-art online trackers. Furthermore, due to the
simplicity of our tracking method, the tracker
updates at a rate of 260 Hz which is over 20x
faster than other state-of-the-art trackers.
Parameters:
\iou_threshold=0.3
Latex Bibtex:
@inproceedings{bewley2016simple,
title={Simple online and realtime tracking},
author={Bewley, Alex and Ge, Zongyuan and Ott,
Lionel and Ramos, Fabio and Upcroft, Ben},
booktitle={2016 IEEE International Conference on
Image Processing (ICIP)},
pages={3464--3468},
year={2016},
organization={IEEE}
}

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 42.52 % 44.01 % 41.31 % 47.30 % 73.93 % 42.83 % 83.04 % 80.75 %

Benchmark TP FP FN
CAR 20325 14067 1676

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 53.15 % 77.75 % 54.23 % 370 40.00 %

Benchmark MT rate PT rate ML rate FRAG
CAR 26.15 % 44.46 % 29.39 % 621

Benchmark # Dets # Tracks
CAR 22001 1189

This table as LaTeX