Journal article
Online multi-person tracking with two-stage data association and online appearance model learning
IET computer vision, v 11(1), pp 87-95
01 Feb 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This study addresses the automatic multi-person tracking problem in complex scenes from a single, static, uncalibrated camera. In contrast with offline tracking approaches, a novel online multi-person tracking method is proposed based on a sequential tracking-by-detection framework, which can be applied to real-time applications. A two-stage data association is first developed to handle the drifting targets stemming from occlusions and people's abrupt motion changes. Subsequently, a novel online appearance learning is developed by using the incremental/ decremental support vector machine with an adaptive training sample collection strategy to ensure reliable data association and rapid learning. Experimental results show the effectiveness and robustness of the proposed method while demonstrating its compatibility with real-time applications.
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Details
- Title
- Online multi-person tracking with two-stage data association and online appearance model learning
- Creators
- Jaeyong Ju - Korea UniversityDaehun Kim - Korea UniversityBonhwa Ku - Korea UniversityDavid K. Han - Office of Naval ResearchHanseok Ko - Korea University
- Publication Details
- IET computer vision, v 11(1), pp 87-95
- Publisher
- Wiley
- Number of pages
- 9
- Grant note
- Korea University
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000396099200010
- Scopus ID
- 2-s2.0-85017505951
- Other Identifier
- 991021930828204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic