CFSORT: improved SORT based on coarse to fine mechanism for object tracking

Jie Zhao

doi:10.1117/12.2680674

31 May 2023 CFSORT: improved SORT based on coarse to fine mechanism for object tracking

Jie Zhao

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 127041M (2023) https://doi.org/10.1117/12.2680674
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

In recent years, object tracking attracts much attention as a upstream task in many specific area. Multi-object tracking is one of them and its challenge are ID switch and ID re-matching due to object occlusion and image quality. In this paper, two possible methods are introduced to improve the baseline. In ReID model, a data augmentation method is built. Its main idea is to discuss and reduce the influence of background by retraining the ReID model. As ReID focuses on the features of objects in most cases, backgrounds can affect the feature metric significantly. In this situation, different objects in different backgrounds are captured and extracted as train data. Specially, a pure background, or background without objects are used in triplet loss to maximum the similarity distance. Besides, an updated SORT is implemented as CFSORT. In CFSORT, features are extracted by retrained ReID and two new processes or units are created. Comparing with the baseline, CFSORT requires lower confidence objects and emphasis a “coarse to fine” process. Those two new units are corresponding to similarity and IOU measurement. Currently, all new detection will be treated equally at first matching action. Similarly, more detection and tracked objects will match in new IOU unit which is stricter than the normal one. As a consequence, the result indicates that CFSORT has an improved performance on several evaluation scores in different scales. Meanwhile, this work can also prove the importance of accepting low confidence.

Citation Download Citation

Jie Zhao "CFSORT: improved SORT based on coarse to fine mechanism for object tracking", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 127041M (31 May 2023); https://doi.org/10.1117/12.2680674

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Education and training

Video

Target detection

Data modeling

Deep learning

Feature extraction

Switches

Show All Keywords

Keywords/Phrases

Search In:

Publication Years