IEEE Access (Jan 2021)

Efficient Online Tracking-by-Detection With Kalman Filter

  • Siyuan Chen,
  • Chenhui Shao

DOI
https://doi.org/10.1109/ACCESS.2021.3124705
Journal volume & issue
Vol. 9
pp. 147570 – 147578

Abstract

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Visual tracking of multiple objects in videos has a promisingly broad application in manufacturing, construction, traffic, logistics, etc., especially in large-scale applications where it is not feasible to attach markers to many objects for traditional, marker-enabled tracking methods. This paper presents a new approach, Kalman-intersection-over-union (KIOU) tracker, for multi-object tracking in videos that integrates a Kalman filter with IOU-based track association methods. The performance of the proposed KIOU tracker is quantitatively evaluated with UA-DETRAC, an open real-world multi-object detection and tracking benchmark. Experimental results show that the KIOU tracker outperforms the leading tracking methods. Additionally, the KIOU tracker has speed comparable to simple area overlap-based track association and quality close to methods with much higher computational costs, demonstrating its potential for online, real-time multi-object tracking.

Keywords