ISLab-PVD : Illegally Parked Vehicle Dataset

Paper Title

Abstract

Illegally parked vehicles on the urban road may create a traffic flow problem as well as a potential traffic accident, such as crashing between parked and other vehicles. Thus, the intelligent traffic monitoring system should be able to prevent this situation by integrating an illegally parked vehicle detection module. However, implementing such a module becomes more challenging due to road environments, such as weather conditions, occlusion, and illumination changing. Hence, this work addresses a method to implement an illegally parked vehicle detection based on the cumulative dual foreground differences from the shortand long-term background models, temporal analysis, vehicle detector, and tracking. The extensive experiments were conducted using both iLIDS and our proposed datasets to evaluate the effectiveness of the proposed method by comparing with other methods. The results showed that the method is effective in detecting illegally parked vehicles and can be considered as part of the intelligent traffic monitoring system.

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Dataset

ISLab-PVD dataset containing 16 video sequences with various challenging scenarios. The situations include crowded scenes, different in lighting conditions, various sizes of vehicles, and night-time detection using the infrared camera. You may download the dataset in this link.

The dataset should be used for personal or research purpose only. If you want to use for commercial purpose, please contact us.

Citation

Wahyono and Kang-Hyun Jo, Cumulative Dual Foreground Differences For Illegally Parked Vehicles Detection, IEEE Transaction on Industrial Informatics, Available online Feb. 7, 2017. DOI:10.1109/TII.2017.2665584.

@ARTICLE{7845683, 
author={W. Wahyono and K. H. Jo}, 
journal={IEEE Transactions on Industrial Informatics}, 
title={Cumulative Dual Foreground Differences For Illegally Parked Vehicles Detection}, 
year={2017}, 
volume={PP}, 
number={99}, 
pages={1-1}, 
doi={10.1109/TII.2017.2665584}, 
ISSN={1551-3203}, 
month={},}

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