Multi-Speaker Tracking from Azure Kinect: Performance Evaluation using Generalized Optimal Sub-Pattern Assignment

Authors

  • Muhammad Atiff Zakwan Bin Mohd Ariffin University of Technology Malaysia image/svg+xml
  • Siti Nur Aisyah Binti Mohd Robi University of Technology Malaysia image/svg+xml
  • Mohd Azri Bin Mohd Izhar University of Technology Malaysia image/svg+xml
  • Norulhusna Binti Ahmad University of Technology Malaysia image/svg+xml

DOI:

https://doi.org/10.11113/oiji2023.11n2.289

Keywords:

Multi-speaker tracking, Azure Kinect device, optimal sub-pattern assignment, body tracking, performance evaluation

Abstract

The software and the hardware features of Azure Kinect show the potential for its application in multiple speaker tracking. We then evaluate the system’s capabilities by conducting tests using our given setup, including various scenarios to evaluate its tracking performance. The tracking performance is calculated using generalized optimal sub-pattern assignment (GOSPA) and multiple objects tracking accuracy (MOTA) metrics. It has been found that the body tracking algorithm can perform well in certain multi-speaker tracking conditions.

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Published

2023-12-18

Issue

Vol. 11 No. 2 (2023): Open International Journal of Informatics (OIJI)

Section

Articles

How to Cite

Multi-Speaker Tracking from Azure Kinect: Performance Evaluation using Generalized Optimal Sub-Pattern Assignment. (2023). Open International Journal of Informatics, 11(2), 188-195. https://doi.org/10.11113/oiji2023.11n2.289