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Parking Eyes: Empirical Study on Video-Based Detection of Lincense Plates and People

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dc.contributor.advisor Dailey, Matthew N. (Chairman) en_US
dc.contributor.author Thiparat, Jirawattanakitja en_US
dc.contributor.other Kiyoshi Honda (Member) en_US
dc.contributor.other Mongkol Ekpanyapong (Member) en_US
dc.date.accessioned 2015-01-12T10:41:41Z
dc.date.available 2015-01-12T10:41:41Z
dc.date.issued 2012-12 en_US
dc.identifier.other AIT Thesis no.CS-12-11 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/404
dc.description.abstract Object detection has applications in many areas of computer vision, including image retrieval and video surveillance. Face and license plate detection are especially key steps in many processing systems nowadays. In this thesis, I develop a system to collect information about cars’ and drivers’ identities in a parking area in order to improve security of parking lots. We mainly experiment with data capture from guardhouses at the entrances and exits of parking areas. In case a crime such as license plate switching or car theft occurs, the system can help provide evidence to point out who the thief is. In my detection experiments, I choose two interesting approaches. The first algorithm is the Adaboost (Adaptive Boosting cascade) training algorithm. This method is very effective and widely used for object detection. The other algorithm is hyper-features. This method has very good results in recognition problems. I apply Adaboost cascading and hyper-features to the two object detection problems. I summarize the overall performance and error patterns of each method in terms of ROC analysis for comparison. en_US
dc.description.sponsorship Royal Thai Government en_US
dc.language.iso eng en_US
dc.subject.lcsh Others en_US
dc.title Parking Eyes: Empirical Study on Video-Based Detection of Lincense Plates and People en_US
dc.type Thesis en_US

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