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A Trainable Object Detection System: Car Detection in Static Images

dc.date.accessioned2004-10-20T20:48:44Z
dc.date.accessioned2018-11-24T10:23:13Z
dc.date.available2004-10-20T20:48:44Z
dc.date.available2018-11-24T10:23:13Z
dc.date.issued1999-10-13en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7173
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7173
dc.description.abstractThis paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.en_US
dc.format.extent5 p.en_US
dc.format.extent17300098 bytes
dc.format.extent2264067 bytes
dc.language.isoen_US
dc.subjectAIen_US
dc.subjectMITen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectpattern recognitionen_US
dc.subjectsmachine learningen_US
dc.subjectobject detectionen_US
dc.subjectcar detectionen_US
dc.titleA Trainable Object Detection System: Car Detection in Static Imagesen_US


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