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Active learning based robust monocular vehicle detection for on-road safety systems

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2 Author(s)
Sayanan Sivaraman ; LISA: Laboratory for Intelligent and Safe Automobiles, University of California, San Diego, USA ; Mohan Manubhai Trivedi

In this paper, the framework is presented for using active learning to train a robust monocular on-road vehicle detector for active safety, based on Adaboost classification and Haar-like rectangular image features. An initial vehicle detector was trained using Adaboost and Haar-like rectangular image features and was very susceptible to false positives. This detector was run on an independent highway dataset, storing true detections and false positives to obtain a selectively sampled training set for the active learning training iteration. Various configurations of the newly trained classifier were tested, experimenting with the trade-off between detection rate and false detection rate. Experimental results show that this method yields a vehicle classifier with a high detection rate and low false detection rate on real data, yielding a valuable addition to environmental awareness for intelligent active safety systems in vehicles.

Published in:

Intelligent Vehicles Symposium, 2009 IEEE

Date of Conference:

3-5 June 2009