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Research Article | Open Access

Robust tracking-by-detection using a selection and completion mechanism

Tsinghua University, Beijing 100084, China.
School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand.
Center of Mathematical Sciences and Applications, Harvard University, Cambridge, Massachusetts, USA.
School of Computer Science and Informatics, Cardiff University, Cardiff, Wales, UK.
Show Author Information

Abstract

It is challenging to track a target continuously in videos with long-term occlusion, or objects which leave then re-enter a scene. Existing tracking algorithms combined with online-trained object detectors perform unreliably in complex conditions, and can only provide discontinuous trajectories with jumps in position when the object is occluded. This paper proposes a novel framework of tracking-by-detection using selection and completion to solve the abovementioned problems. It has two components, tracking and trajectory completion. An offline-trained object detector can localize objects in the same category as the object being tracked. The object detector is based on a highly accurate deep learning model. The object selector determines which object should be used to re-initialize a traditional tracker. As the object selector is trained online, it allows the framework to be adaptable. During completion, a predictive non-linear autoregressive neural network completes any discontinuous trajectory. The tracking component is an online real-time algorithm, and the completion part is an after-the-event mechanism. Quantitative experiments show a significant improvement in robustness over prior state-of-the-art methods.

Keywords

object trackingdetectionproposal selectiontrajectory completion

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Computational Visual Media
Volume 3 Issue 3,
September 2017
Pages 285-294
DOI: 10.1007/s41095-017-0083-7
Cite this article:
Fan R, Zhang F-L, Zhang M, et al. Robust tracking-by-detection using a selection and completion mechanism. Computational Visual Media, 2017, 3(3): 285-294. https://doi.org/10.1007/s41095-017-0083-7

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Revised: 05 February 2017
Accepted: 07 April 2017
Published: 18 May 2017
© The Author(s) 2017

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