Occluded illegally parked vehicle detection and long term tracking (Conference Presentation)

Xing Gao; Philip M. Birch; Rupert C. Young; Chris R. Chatwin

doi:10.1117/12.2558549

27 April 2020 Occluded illegally parked vehicle detection and long term tracking (Conference Presentation)

Xing Gao, Philip M. Birch, Rupert C. Young, Chris R. Chatwin

Author Affiliations +

Proceedings Volume 11400, Pattern Recognition and Tracking XXXI; 114000C (2020) https://doi.org/10.1117/12.2558549
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

We 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.

Conference Presentation

Citation Download Citation

Xing Gao, Philip M. Birch, Rupert C. Young, and Chris R. Chatwin "Occluded illegally parked vehicle detection and long term tracking (Conference Presentation)", Proc. SPIE 11400, Pattern Recognition and Tracking XXXI, 114000C (27 April 2020); https://doi.org/10.1117/12.2558549

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KEYWORDS

Algorithm development

Detection and tracking algorithms

Electronic filtering

Light sources and illumination

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