Probabilistic tracklet scoring and inpainting for multiple object tracking

Fatemeh Saleh, Sadegh Aliakbarian, Hamid Rezatofighi, Mathieu Salzmann, Stephen Gould

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

73 Citations (Scopus)

Abstract

Despite the recent advances in multiple object tracking (MOT), achieved by joint detection and tracking, dealing with long occlusions remains a challenge. This is due to the fact that such techniques tend to ignore the long-term motion information. In this paper, we introduce a probabilistic autoregressive motion model to score tracklet proposals by directly measuring their likelihood. This is achieved by training our model to learn the underlying distribution of natural tracklets. As such, our model allows us not only to assign new detections to existing tracklets, but also to inpaint a tracklet when an object has been lost for a long time, e.g., due to occlusion, by sampling tracklets so as to fill the gap caused by misdetections. Our experiments demonstrate the superiority of our approach at tracking objects in challenging sequences; it outperforms the state of the art in most standard MOT metrics on multiple MOT benchmark datasets, including MOT16, MOT17, and MOT20.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
EditorsMargaux Masson-Forsythe, Eric Mortensen
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages14324-14334
Number of pages11
ISBN (Electronic)9781665445092
ISBN (Print)9781665445108
DOIs
Publication statusPublished - 2021
EventIEEE Conference on Computer Vision and Pattern Recognition 2021 - Online, Virtual, Online, United States of America
Duration: 19 Jun 202125 Jun 2021
https://cvpr2021.thecvf.com/ (Website)
https://ieeexplore.ieee.org/xpl/conhome/9577055/proceeding (Proceedings)

Publication series

NameProceedings 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR 2021
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
ISSN (Print)2575-7075
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2021
Abbreviated titleCVPR 2021
Country/TerritoryUnited States of America
CityVirtual, Online
Period19/06/2125/06/21
Internet address

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