Appearance-Based Object Detection Under Varying Environmental Conditions

Practical surveillance systems deployed in urban scenarios need to operate 24/7 under a wide range of environmental conditions. As modern video analytics shift from blob-based to object-centered architectures, appearance-based object detection under different weather conditions and lighting effects emerges as a critical yet largely unaddressed problem. This paper investigates this research topic, using as a case study the problem of vehicle detection in urban surveillance environments. In particular, we show that a simple and efficient Winsorized lighting correction technique improves performance significantly when outliers due to shadows, specularities, headlights, and occluders are present. Moreover, we demonstrate that a self-training mechanism utilizing a balanced training set automatically acquired from the target domain yields superior performance. Our experimental results are carried out on a novel dataset of vehicle images collected from a public traffic camera and categorized according to multiple environmental conditions.