A two-step approach for detecting individuals within dense crowds

Abstract

This paper proposes a two-step approach for detecting individuals within dense crowds. First step uses an offline-trained Viola-type head detector in still color images of dense crowds in a cluttered background. In the second step, which aims to reduce false alarm rates at same detection rates, color bin images are constructed from normalized rg color histograms of the detected windows in the first step. Haar-like features extracted from these color bin images are input to a trained cascade of boosted classifiers to separate correct detections from false alarms. Experimental results of both steps are presented as Receiver Operating Characteristics (ROC) curves, in comparison with recent related work. Our proposed two-step approach is able to attain a high detection rate of 90.0%, while maintaining false alarm rate below 40.0%, as compared to other work which attains a high 70.0% false alarm rate when detection rate is still below 90.0%. © 2008 Springer-Verlag.