Multi-object tracking with an adaptive generalized labeled multi-Bernoulli filter

Cong Thanh Do, Tran Thien Dat Nguyen, Diluka Moratuwage, Changbeom Shim, Yon Dohn Chung

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process. Further, the information on locations where the objects appear, their detection probabilities, and the statistics of the sensor's false alarms significantly influence the tracking accuracy of the filter. However, this information is usually assumed to be known and provided by the users. In this paper, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter which can track multiple objects without prior knowledge of the aforementioned information. Experimental results show that the performance of the proposed filter is comparable to an ideal GLMB filter supplied with correct information of the tracking scenarios.

Original languageEnglish
Article number108532
JournalSignal Processing
Publication statusPublished - 2022 Jul


  • Adaptive birth model
  • Bootstrapping
  • GLMB Filter
  • Multi-object Bayes filter
  • Unknown clutter rate
  • Unknown detection probability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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