Online Multi-Object Tracking via Robust Collaborative Model and Sample Selection
- Title
- Online Multi-Object Tracking via Robust Collaborative Model and Sample Selection
- Author
- 임종우
- Keywords
- Multi-object tracking; Particle filter; Collaborative model; Sample selection; Sparse representation
- Issue Date
- 2017-01
- Publisher
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Citation
- COMPUTER VISION AND IMAGE UNDERSTANDING, v. 154, page. 94-107
- Abstract
- The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a collaborative model between a pre-trained object detector and a number of single object online trackers within the particle filtering framework. For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker. We present a motion model that incorporates the associated detections with object dynamics. Furthermore, we propose an effective sample selection scheme to update the appearance model of each tracker. We use discriminative and generative appearance models for the likelihood function and data association, respectively. Experimental results show that the proposed scheme generally outperforms state-of-the-art methods. (C) 2016 Elsevier Inc. All rights reserved.
- URI
- https://www.sciencedirect.com/science/article/pii/S1077314216300996?via%3Dihubhttps://repository.hanyang.ac.kr/handle/20.500.11754/112455
- ISSN
- 1077-3142; 1090-235X
- DOI
- 10.1016/j.cviu.2016.07.003
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML