Real-Time and Online Segmentation Multi-Target Tracking with Track Revival Re-Identification
(2021) 5. p.777-784- Abstract (Swedish)
- The first online segmentation multi-target tracking algorithm with reported real-time speeds is presented. Based on the popular and fast bounding box based tracker SORT, our method called SORTS is able to utilize segmentations for tracking while keeping the real-time speeds. To handle occlusions, which neither SORT nor SORTS do, we also present SORTS+RReID, an optional extension which uses ReID vectors to revive lost tracks from SORTS to handle occlusions. Despite only computing ReID vectors for 6.9% of the detections, ID switches are decreased by 45%. We evaluate on the MOTS dataset and run at 54.5 and 36.4 FPS for SORTS and SORT+RReID respectively, while keeping 78-79% of the sMOTSA of the current state of the art, which runs at 0.3 FPS.... (More)
- The first online segmentation multi-target tracking algorithm with reported real-time speeds is presented. Based on the popular and fast bounding box based tracker SORT, our method called SORTS is able to utilize segmentations for tracking while keeping the real-time speeds. To handle occlusions, which neither SORT nor SORTS do, we also present SORTS+RReID, an optional extension which uses ReID vectors to revive lost tracks from SORTS to handle occlusions. Despite only computing ReID vectors for 6.9% of the detections, ID switches are decreased by 45%. We evaluate on the MOTS dataset and run at 54.5 and 36.4 FPS for SORTS and SORT+RReID respectively, while keeping 78-79% of the sMOTSA of the current state of the art, which runs at 0.3 FPS. Furthermore, we include an experiment using a faster instance segmentation method to explore the feasibility of a complete real-time detection and tracking system. Code is available: https://github.com/ahrnbom/sorts. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5f6bf791-d5cd-448e-8afe-5e578a356583
- author
- Ahrnbom, Martin LU ; Nilsson, Mikael LU and Ardö, Håkan LU
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
- volume
- 5
- pages
- 777 - 784
- external identifiers
-
- scopus:85102977844
- ISBN
- 978-989-758-488-6
- DOI
- 10.5220/0010190907770784
- language
- Swedish
- LU publication?
- yes
- id
- 5f6bf791-d5cd-448e-8afe-5e578a356583
- date added to LUP
- 2021-02-08 11:08:30
- date last changed
- 2022-05-04 23:41:21
@inproceedings{5f6bf791-d5cd-448e-8afe-5e578a356583, abstract = {{The first online segmentation multi-target tracking algorithm with reported real-time speeds is presented. Based on the popular and fast bounding box based tracker SORT, our method called SORTS is able to utilize segmentations for tracking while keeping the real-time speeds. To handle occlusions, which neither SORT nor SORTS do, we also present SORTS+RReID, an optional extension which uses ReID vectors to revive lost tracks from SORTS to handle occlusions. Despite only computing ReID vectors for 6.9% of the detections, ID switches are decreased by 45%. We evaluate on the MOTS dataset and run at 54.5 and 36.4 FPS for SORTS and SORT+RReID respectively, while keeping 78-79% of the sMOTSA of the current state of the art, which runs at 0.3 FPS. Furthermore, we include an experiment using a faster instance segmentation method to explore the feasibility of a complete real-time detection and tracking system. Code is available: https://github.com/ahrnbom/sorts.}}, author = {{Ahrnbom, Martin and Nilsson, Mikael and Ardö, Håkan}}, booktitle = {{Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}}, isbn = {{978-989-758-488-6}}, language = {{swe}}, pages = {{777--784}}, title = {{Real-Time and Online Segmentation Multi-Target Tracking with Track Revival Re-Identification}}, url = {{http://dx.doi.org/10.5220/0010190907770784}}, doi = {{10.5220/0010190907770784}}, volume = {{5}}, year = {{2021}}, }