Multiplex Labeling Graph for Near-Online Tracking in Crowded Scenes

Yang Zhang, Hao Sheng, Yubin Wu, Shuai Wang, Wei Ke, Zhang Xiong

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)

Abstract

In recent years, the demand for intelligent devices related to the Internet of Things (IoT) is rapidly increasing. In the field of computer vision, many algorithms have been preinstalled in IoT devices to achieve higher efficiency, such as face recognition, area detection, target tracking, etc. Tracking is an important but complex task that needs high efficiency solutions in real applications. There is a common assumption that detection can only represent one pedestrian to describe nonoverlapping in physical space. In fact, the pixels of the image do not exactly correspond to the positions in the real world. In order to overcome the limitation of this assumption, we remove this unreasonable assumption and present a novel idea that each detector response can have multiple labels to describe different targets at the same time. Therefore, we propose a graph-based method for near-online tracking in this article. We introduce a detection multiplexing method for tracking in the monocular image and propose a multiplex labeling graph (MLG) model. Each node in MLG has the ability to represent multiple targets. In addition, we improve the shortage of graph-based trackers in using temporal features. We construct long short-term memory networks to model motion and appearance features for MLG optimization. On the public multiobject tracking challenge benchmark, our near-online method gains satisfactory efficiency and achieves state-of-the-art results without additional private detection as well.

Original languageEnglish
Article number9098857
Pages (from-to)7892-7902
Number of pages11
JournalIEEE Internet of Things Journal
Volume7
Issue number9
DOIs
Publication statusPublished - Sept 2020

Keywords

  • Data association
  • long short-term memory (LSTM)
  • multiplex labeling graph (MLG)
  • near-online tracking
  • spatial-temporal features

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