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Occluded illegally parked vehicle detection and long term tracking (Conference Presentation)
conference contribution
posted on 2023-06-10, 07:03 authored by Xing Gao, Phil BirchPhil Birch, Rupert CD Young, Chris ChatwinChris ChatwinWe propose a method of detecting and tracking occluded illegally parked vehicles. The method used a deep learning framework that can detect and track moving vehicles. To obtain the long term tracking of stationary vehicles the process must be capable of withstanding large changes in lighting, weather and large amounts of occlusion from passing vehicles. A modified dense SIFT descriptor algorithm has been developed. This compares the current frame with the background and removes objects in motion. The tracking of the occluded illegally parked vehicle is achieved by YOLO version 3 algorithm, combined with a predictive filter. For each illegally parked vehicle, the occluded portion is not used for feature point matching. Based on the matching result, the occluded illegal vehicle can be tracked. Our approach tested performance on a public database(i-LIDS) and the results indicate the method produces a very high accuracy compared to other published work.
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Publication status
- Published
Journal
Pattern Recognition and Tracking XXXIPublisher
SPIEExternal DOI
Volume
11400Page range
12Event name
Pattern Recognition and Tracking XXXIEvent location
OnlineEvent type
conferenceEvent date
27 Apr 2020 - 1 May 2020Department affiliated with
- Engineering and Design Publications
Full text available
- No
Peer reviewed?
- No
Editors
Mohammad S AlamLegacy Posted Date
2023-05-16Usage metrics
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