A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and people detection 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 di erences. These feature vectors are then used to train a support vector machine classi er. The detection of cars in images is an important step in applications such as tra c 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. Copyright c Massachusetts Institute of Technology, 1999 This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and at the Arti cial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsored by ONR/MURI grant N00014-95-1-0600. Additional support is provided by Eastman Kodak Company, DaimlerChrysler, Siemens Corporate Research, Inc. and AT&T.

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