A Trainable Object Detection System: Car Detection in Static Images
Author(s)
Papageorgiou, Constantine P.
Poggio, Tomaso
Date Issued
October 13, 1999
Series/Report no.
AIM-1673
CBCL-180
Abstract
This 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.
Subjects
AI
MIT
Artificial Intelligence
pattern recognition
smachine learning
object detection
car detection
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