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Online multi-object tracking with efficient track drift and fragmentation handling.

Authors
  • Ju, Jaeyong
  • Kim, Daehun
  • Ku, Bonhwa
  • Han, David K
  • Ko, Hanseok
Type
Published Article
Journal
Journal of the Optical Society of America A
Publisher
The Optical Society
Publication Date
Feb 01, 2017
Volume
34
Issue
2
Pages
280–293
Identifiers
DOI: 10.1364/JOSAA.34.000280
PMID: 28157856
Source
Medline
License
Unknown

Abstract

This paper addresses the problem of multi-object tracking in complex scenes by a single, static, uncalibrated camera. Tracking-by-detection is a widely used approach for multi-object tracking. Challenges still remain in complex scenes, however, when this approach has to deal with occlusions, unreliable detections (e.g., inaccurate position/size, false positives, or false negatives), and sudden object motion/appearance changes, among other issues. To handle these problems, this paper presents a novel online multi-object tracking method, which can be fully applied to real-time applications. First, an object tracking process based on frame-by-frame association with a novel affinity model and an appearance update that does not rely on online learning is proposed to effectively and rapidly assign detections to tracks. Second, a two-stage drift handling method with novel track confidence is proposed to correct drifting tracks caused by the abrupt motion change of objects under occlusion and prolonged inaccurate detections. In addition, a fragmentation handling method based on a track-to-track association is proposed to solve the problem in which an object trajectory is broken into several tracks due to long-term occlusions. Based on experimental results derived from challenging public data sets, the proposed method delivers an impressive performance compared with other state-of-the-art methods. Furthermore, additional performance analysis demonstrates the effect and usefulness of each component of the proposed method.

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