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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/2D869BA2-66D3-4936-9319-9680FCEE2DD3 | - |
| Identifier | http://www.tuc.gr/fileadmin/users_data/elci/Kalaitzakis/J.25.pdf | - |
| Identifier | https://doi.org/10.1016/S0262-8856(03)00004-0 | - |
| Language | en | - |
| Extent | 23 | en |
| Title | A survey of video processing techniques for traffic applications. | en |
| Creator | Kastrinaki V. | en |
| Creator | Zervakis Michalis | en |
| Creator | Ζερβακης Μιχαλης | el |
| Creator | Kalaitzakis Kostas | en |
| Creator | Καλαϊτζακης Κωστας | el |
| Publisher | Elsevier | en |
| Description | Δημοσίευση σε επιστημονικό περιοδικό | el |
| Content Summary | Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Research in several fields of traffic applications has resulted in a wealth of video processing and analysis methods. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. In general, systems developed for these areas must integrate, amongst their other tasks, the analysis of their static environment (automatic lane finding) and the detection of static or moving obstacles (object detection) within their space of interest. In this paper we present an overview of image processing and analysis tools used in these applications and we relate these tools with complete systems developed for specific traffic applications. More specifically, we categorize processing methods based on the intrinsic organization of their input data (feature-driven, area-driven, or model-based) and the domain of processing (spatial/frame or temporal/video). Furthermore, we discriminate between the cases of static and mobile camera. Based on this categorization of processing tools, we present representative systems that have been deployed for operation. Thus, the purpose of the paper is threefold. First, to classify image-processing methods used in traffic applications. Second, to provide the advantages and disadvantages of these algorithms. Third, from this integrated consideration, to attempt an evaluation of shortcomings and general needs in this field of active research. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-09-30 | - |
| Date of Publication | 2003 | - |
| Subject | Detection, Traffic | en |
| Subject | Monitoring, Traffic | en |
| Subject | Surveillance, Traffic | en |
| Subject | Traffic detection | en |
| Subject | Traffic surveillance | en |
| Subject | traffic monitoring | en |
| Subject | detection traffic | en |
| Subject | monitoring traffic | en |
| Subject | surveillance traffic | en |
| Subject | traffic detection | en |
| Subject | traffic surveillance | en |
| Subject | Automatic vehicle guidance | en |
| Subject | Automatic lane finding | en |
| Subject | Object detection | en |
| Subject | Dynamic scene analysis | en |
| Bibliographic Citation | V. Kastrinaki, M. Zervakis and K. Kalaitzakis, "A survey of video processing techniques for traffic applications," Image and Vision Computing, vol. 21, no. 4, pp. 359-381, Apr. 2003. doi:10.1016/S0262-8856(03)00004-0 | en |
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