期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 13, 期 5, 页码 2464-2473出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2017.2665584
关键词
Cumulative dual foreground (CDF) difference; Gaussian mixture model; intelligent traffic monitoring; illegally parked vehicle detection; long term background model; short term background model; temporal analysis; vehicle detector (VD)
类别
资金
- National Research Foundation of Korea (NRF) - Korean Government [2016R1D1A1A02937579]
- National Research Foundation of Korea [2016R1D1A1A02937579] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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 short-and 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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