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Open AccessArticle

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

1
School of Information and Electronic Engineering; Zhejiang University of Science and Technology, Hangzhou 310023, China
2
School of Mechanical and Energy Engineering; Zhejiang University of Science and Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3638; https://doi.org/10.3390/s20133638
Received: 29 April 2020 / Revised: 17 June 2020 / Accepted: 23 June 2020 / Published: 29 June 2020
(This article belongs to the Section Intelligent Sensors)
The main task for real-time vehicle tracking is establishing associations with objects in consecutive frames. After occlusion occurs between vehicles during the tracking process, the vehicle is given a new ID when it is tracked again. In this study, a novel method to track vehicles between video frames was constructed. This method was applied on driving recorder sensors. The neural network model was trained by YOLO v3 and the system collects video of vehicles on the road using a driving data recorder (DDR). We used the modified Deep SORT algorithm with a Kalman filter to predict the position of the vehicles and to calculate the Mahalanobis, cosine, and Euclidean distances. Appearance metrics were incorporated into the cosine distances. The experiments proved that our algorithm can effectively reduce the number of ID switches by 29.95% on the model trained on the BDD100K dataset, and it can reduce the number of ID switches by 32.16% on the model trained on the COCO dataset. View Full-Text
Keywords: YOLO; DDR; vehicle tracking; occlusion; ID switches YOLO; DDR; vehicle tracking; occlusion; ID switches
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MDPI and ACS Style

Zhao, Y.; Zhou, X.; Xu, X.; Jiang, Z.; Cheng, F.; Tang, J.; Shen, Y. A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors. Sensors 2020, 20, 3638.

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