Research Repository

Learn-select-track: An approach to multi-object tracking

Makhura, Onalenna J and Woods, John C (2019) 'Learn-select-track: An approach to multi-object tracking.' Signal Processing: Image Communication, 74. pp. 153-161. ISSN 0923-5965

1s20S092359651830969Xmain.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB) | Preview


Object tracking algorithms rely on user input to learn the object of interest. In multi-object tracking, this can be a challenge when the user has to provide a lot of locations to track. This paper presents a new approach that reduces the need for user input in multi-tracking. The approach uses density based clustering to analyse the colours in one frame and find the best separation of colours. The colours selected from the detection are learned and used in subsequent frames to track the colours through the video. With this training approach, the user interaction is limited to selecting the colours rather than selecting the multiple location to be tracked. The training algorithm also provides online training even when training on thousands of features.

Item Type: Article
Uncontrolled Keywords: Multi-object tracking; Object colours; Density-based clustering; Low level local features
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 01 Mar 2019 09:36
Last Modified: 18 Aug 2022 10:32

Actions (login required)

View Item View Item