Online multi‐person tracking with two‐stage data association and online appearance model learning. Issue 1 (16th August 2016)

• Record Type: Journal Article
• Authors: Ju, Jaeyong; Kim, Daehun; Ku, Bonhwa; Han, David K.; Ko, Hanseok
• Is Part Of: IET computer vision. Volume 11:Issue 1(2017)
• Page Start: 87
• Page End: 95
• Publication Date: 2016-08-16
• Subjects: object tracking -- sensor fusion -- learning (artificial intelligence) -- image sensors -- object detection -- support vector machines; online multiperson tracking method -- two-stage data association -- online appearance model learning -- automatic multiperson tracking problem -- single camera -- static camera -- uncalibrated camera -- sequential tracking-by-detection framework -- drifting targets -- incremental support vector machine -- decremental support vector machine -- adaptive training sample collection strategy -- rapid learning; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2016.0068
• Languages: English
• ISSNs: 1751-9632

Abstract

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.