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Real-time occlusion tolerant detection of illegally parked vehicles

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Abstract

Illegally parked vehicle detection systems are considered crucial elements in the development of any video-surveillance based traffic-management system. The major challenges in this task lie in making the end solution real time, illumination invariant and occlusion tolerant. A two-stage application framework is presented which efficiently identifies vehicles parked illegally in restricted parking-zones. A real-time approach has been followed and an improved foreground segmentation method based on Segmentation History Images (SHI) is developed to identify stationary objects. A three step pixel based classification method is applied on the background segmentation output to segment adjacent moving pixels that become stationary for certain periods of time. The process then locks on to all identified stationary pixel patches, parts of which overlap with the regions of interest marked interactively a priori. The second stage of the process is applied subsequently to track all the stationary pixel patches detected during the first stage using an adaptive edge orientation based tracking method. Experimental results show that the tracking technique gives more than a 90% detection success rate, even if objects are partially occluded. The technique has been tested on the UK Home Office i-LIDS Parked Vehicle video sequences along with the University of Sussex Traffic Dataset and results are compared with other available state of the art methods.

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Authors and Affiliations

  1. School of Engineering and Informatics, University of Sussex, Falmer, UK

    Waqas Hassan, Philip Birch, Rupert Young & Chris Chatwin

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  1. Waqas Hassan
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  2. Philip Birch
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  3. Rupert Young
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  4. Chris Chatwin
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Correspondence to Waqas Hassan.

Additional information

Recommended by Editorial Board member Dong-Joong Kang under the direction of Editor Hyouk Ryeol Choi.

Waqas Hassan received his Bachelors degree in Information Technology from National University of Science and Technology (NUST) Pakistan in 2004 and his M.S. degree in Embedded Digital Systems from University of Sussex, United Kingdom in 2006. He is currently working towards a Ph.D. degree in object tracking and event recognition in real time vision systems from University of Sussex. His research interests include tracking of human and objects, intelligent surveillance systems and pattern recognition.

Philip Birch is a lecturer at the University of Sussex in the Department of Engineering and Design. He has made research contributions in optoelectronics and signal processing. He has interests in optical signal processing, image processing and computer vision. This has been applied to applications such target detection, microscopy and video event detection.

Rupert Young is currently a Reader at the University of Sussex; he graduated from Glasgow University, from where he also gained his Ph.D. in coherent optical signal processing. Since 1995 he has been with the School of Engineering and Design, University of Sussex. He has published over 200 refereed journal and conference papers on various aspects of digital signal and image processing, pattern recognition and electro-optics. He teaches masters degree modules in Digital Signal Processing and Fibre Optic Communications. He is a member of the Society of Photo-Optical and Instrumentation Engineers and the Optical Society of America.

Chris Chatwin holds the Chair of Industrial Informatics at the University of Sussex, UK; where, inter alia, he is the Director of the “iis Research Centre.” and the Laser and Photonics Systems Engineering Research Group. He has published widely on: computer vision, secure communications, and biometrics for authentication. He is also a member of: the Institution of Electrical and Electronic Engineers; British Computer Society. He is a Chartered Engineer, Euro-Engineer, Chartered Physicist, Fellow of: The Institution of Engineering Technology, The Institution of Mechanical Engineers, The Institute of Physics and The Royal Society for Arts, Manufacture and Commerce.

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Hassan, W., Birch, P., Young, R. et al. Real-time occlusion tolerant detection of illegally parked vehicles. Int. J. Control Autom. Syst. 10, 972–981 (2012). https://doi.org/10.1007/s12555-012-0514-2

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  • DOI: https://doi.org/10.1007/s12555-012-0514-2

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