Efficient 24/7 object detection in surveillance videos

Abstract

We address the problem of 24/7 object detection in urban surveillance videos, which presents unique challenges due to significant object appearance variations caused by lighting effects such as shadows and specular reflections, object pose variation, multiple weather conditions, and different times of the day. Rather than training a generic detector and adapting its parameters over time to handle all these variations, we rely on a large set of complementary and extremely efficient detector models, covering multiple overlapping appearance subspaces. At run time, our method continuously selects the most suitable detectors for a given scene and condition, using a novel approach inspired by parametric background modeling algorithms. We provide a comprehensive experimental analysis to show the effectiveness of our approach, considering traffic monitoring as our application domain. Our system runs at 100 frames per second on a standard laptop computer.