Simple online and realtime tracking

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 t...

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Bibliographic Details
Published inProceedings - International Conference on Image Processing pp. 3464 - 3468
Main Authors Bewley, Alex, Zongyuan Ge, Ott, Lionel, Ramos, Fabio, Upcroft, Ben
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Benchmark testing
Complexity theory
Computer Vision
Data Association
Detection
Detectors
Kalman filters
Multiple Object Tracking
Target tracking
Visualization
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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7533003