Simple online and realtime tracking with a deep association metric

Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer period...

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Bibliographic Details
Published in2017 IEEE International Conference on Image Processing (ICIP) pp. 3645 - 3649
Main Authors Wojke, Nicolai, Bewley, Alex, Paulus, Dietrich
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Cameras
Computer Vision
Data Association
Extraterrestrial measurements
Kalman filters
Multiple Object Tracking
Tracking
Uncertainty
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ISSN2381-8549
DOI10.1109/ICIP.2017.8296962

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Summary:Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer periods of occlusions, effectively reducing the number of identity switches. In spirit of the original framework we place much of the computational complexity into an offline pre-training stage where we learn a deep association metric on a largescale person re-identification dataset. During online application, we establish measurement-to-track associations using nearest neighbor queries in visual appearance space. Experimental evaluation shows that our extensions reduce the number of identity switches by 45%, achieving overall competitive performance at high frame rates.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8296962