The Center for Vision Technologies (CVT) develops and applies its algorithms and hardware to be able to see better with computational sensing, understand the scene using 2D/3D reasoning, understand and interact with humans using interactive intelligent systems, support teamwork through collaborative autonomy, mine big data with multi-modal data analytics and continuously learn through machine learning.

Recent developments from CVT include core machine learning algorithms in various areas such as learning with fewer labels, predictive machine learning for handling surprise and novel situations, lifelong learning, reinforcement learning using semantics and robust/explainable artificial intelligence. 

SmartVision imaging systems use semantic processing/multi-modal sensing and embedded low-power processing for machine learning to automatically adapt and capture good quality imagery and information streams in challenging and degraded visual environments.

Multi-sensor navigation systems are used for wide-area augmented reality and provide GPS-denied localization for humans and mobile platforms operating in air, ground, naval, and subterranean environments. CVT has extended its navigation and 3D modeling work to include semantic reasoning, making it more robust to changes in the scene. Collaborative autonomy systems can use semantic reasoning, enabling platforms to efficiently exchange dynamic scene information with each other and allow a single user to control many robotic platforms using high-level directives.

Human behavior understanding is used to assess human state and emotions (e.g., in the Toyota 2020 concept car) and to build full-body, multi-modal (speech, gesture, gaze, etc.) human-computer interaction systems. 

Multi-modal data analytics systems are used for fine-grain object recognition, activity, and change detection and search in cluttered environments.