Gao, X, Birch, P M, Young, R C D and Chatwin, C R (2020) Illegally parked vehicle detection using deep learning and key-point tracking. 9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019), London UK, 16-18th December 2019. Published in: IET Conference Publications. 2019 (CP760) 7-12. Institution of Engineering and Technology ISBN 9781839531095

PDF - Accepted Version Download (1MB)

Abstract

In this paper, we present a method for identifying and tracking illegally parked vehicles. This approach is based on deep learning for vehicles detection and hand crafted descriptors for the tracking which are designed to cope with occlusions. The tracking of the parked vehicle is achieved by key-point extraction of the detected vehicles and feature point matching. For each frame, a bounding box was generated to represent the vehicle and feature points extracted in that area. All parked vehicles have a unique ID which was generated by the Hungarian algorithm and Kalman filter, and the parked vehicle with the same ID was matched frame by frame. Based on this matching result, the stationary vehicles in the forbidden area can be tracked. Our approach tested efficiency and robustness on a public database and is shown to produce state of the art results.

Item Type:	Conference Proceedings
Additional Information:	This paper is a postprint of a paper submitted to and accepted for publication in IET Conference Publications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
SWORD Depositor:	Mx Elements Account
Depositing User:	Mx Elements Account
Date Deposited:	09 Jan 2023 12:08
Last Modified:	11 Jan 2023 08:49
URI:	http://sro.sussex.ac.uk/id/eprint/110038

View download statistics for this item

📧 Request an update