RI: Small: Modeling and Learning Visual Similarities Under Adverse Visual Conditions

Northwestern University, Evanston IL

Investigators

Abstract

In many emerging applications such as autonomous/assisted driving, intelligent video surveillance, and rescue robots, the performances of visual sensing and analytics are largely jeopardized by various adverse visual conditions in complex unconstrained environments, e.g., bad weather and illumination conditions. This project studies how and to what extend such adverse visual conditions can be coped with. It will advance and enrich the fundamental research of computer vision, and bring significant impact on developing "all-weather"computer vision systems that benefit security/safety, autonomous driving, and robotics. The project contributes to education through curriculum development, student training, and knowledge dissemination. It also includes interactions with K-12 students for participation and research opportunities. This research seeks innovative solution to overcome adverse visual conditions for visual sensing and analytics. It explores a unified approach that avoids explicit image restoration that is in general computationally demanding. It is focused on learning the "alignment" between the two image spaces under adverse and normal conditions, rather than learn everything from scratch. Acting on low-quality data directly without image restoration, this research leads to innovative and computationally efficient solutions to handle adverse visual conditions. Visual restoration can also be done as by-products, and the same approach also provides a general solution to target attribute estimation. The research is focused on: (1) constructing a principled model, called space alignment that models and learns visual similarity, and its theoretical foundation, (2) developing new effective visual matching and tracking approaches based on learning the appropriate visual similarity under various adverse visual condition, (3) investigating visual attribute estimation and identification via learning reconstruction-based visual regression, and (4) developing effective and efficient tools and prototype systems for visual detection, identification, tracking and recognition.