Abstract
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.
Highlights
Tracking of assets and related personnel are of practical value in a variety of usage cases, including traffic, healthcare, storage, logistics, construction and manufacturing sites, and efficient tracking methods are constantly deemed as a significant topic of research
We further show that this approach is suitable for real-time applications as it achieves comparable performance in multi-object tracking accuracy (MOTA), track fragmentation (FM) and identity switches (IDs) metrics with the current leading result while saving up to 80% of computational resources by using less input frame rate
In observation of the aforementioned challenges faced by the IOU tracker, we propose the KIOU tracker by extending the IOU tracker in seek of addressing the challenges of missed detections and low frames per second (FPS), while preserving the merits of the original approach
Summary
Tracking of assets and related personnel are of practical value in a variety of usage cases, including traffic, healthcare, storage, logistics, construction and manufacturing sites, and efficient tracking methods are constantly deemed as a significant topic of research Different tracking technologies, such as barcodes and radio-frequency identification (RFID) tags [1], Ultra WideBand (UWB) technology [2], global and indoor positioning system [3], ultrasonic sensors [4], RGB [5], RGBD [6], or depth-only cameras [7] as well as 3D structures calculated from Structure-from-Motion (SfM) algorithm [8], have been developed, deployed and commercialized at various sites. Reviewers value marker-enabled indoor tracking method, including RFID, and UWB, for their reliability and localization range over camera tracking enabled by basic machine vision applications including barcode and QR-code
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