Efficient Online Tracking-by-Detection With Kalman Filter
Siyuan Chen, Chenhui Shao
Abstract
Fast and reliable visual tracking of multiple objects in videos has a promisingly broad area of application in manufacturing, construction, traffic, logistics, etc., especially so 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 comparable to methods with much higher computational costs, demonstrating its potential for online, real-time multi-object tracking.