Boosting Object Detection Performance in Crowded Surveillance Videos

Abstract

We present a novel approach to automatically create efficient and accurate object detectors tailored to work well on specific video surveillance cameras (specific-domain detectors), using samples acquired with the help of a more expensive, general-domain detector (trained using images from multiple cameras). Our method requires no manual labels from the target domain. We automatically collect training data using tracking over short periods of time from high-confidence samples selected by the general-domain detector. In this context, a novel confidence measure is proposed for detectors based on a cascade of classifiers, which are frequently adopted for computer vision applications that require real-time processing. We demonstrate our proposed approach on the problem of vehicle detection in crowded surveillance videos, showing that an automatically generated detector significantly outperforms the original general-domain detector with much less feature computations.