Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
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.
Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
Hagen Soltau, Lidia Mangu, et al.
ASRU 2011
Diganta Misra, Muawiz Chaudhary, et al.
CVPRW 2024
Hans-Werner Fink, Heinz Schmid, et al.
Journal of the Optical Society of America A: Optics and Image Science, and Vision