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http://dx.doi.org/10.17662/ksdim.2021.17.4.085

Deep-learning Sliding Window Based Object Detection and Tracking for Generating Trigger Signal of the LPR System  

Kim, Jinho (경일대학교 전자공학과)
Publication Information
Journal of Korea Society of Digital Industry and Information Management / v.17, no.4, 2021 , pp. 85-94 More about this Journal
Abstract
The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.
Keywords
Vehicle Detection; Vehicle Tracking; Deep-learning; LPR Trigger Signal;
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