The Center for Vision Technologies
SRI’s Center for Vision Technologies creates fundamental computer vision solutions based on leading-edge technologies, leveraging a wide variety of sensors and computation platforms.
Recent developments from the Center for Vision Technologies
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. CVT does both early-stage research and developmental work to build prototype solutions that impact government and commercial markets, including defense, healthcare, automotive and more. Numerous companies have been spun-off from CVT technology successes.
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.
Our work
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Pedestrian Detection from Moving Unmanned Ground Vehicles
SRI’s vision-based systems enable safe operations of moving unmanned ground vehicles around stationary and moving people in urban/cluttered environments. Under the Navy Explosive Ordnance Disposal project, SRI has developed a real-time, fused-sensor system that significantly improves stationary and dynamic object detection, pedestrian classification, and tracking capabilities from a moving unmanned ground vehicle (UGV). The system […]
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Vision and Language Navigation
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments SRI International has developed a new learning-based approach to enable the mobile robot to resemble human capabilities in semantic understanding. The robot can employ semantic scene structures to reason about the world and pay particular attention to relevant semantic landmarks to develop navigation strategies. […]
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Featured Innovator: Yi Yao
Dr. Yao loves to explore new frontiers, and SRI loves to innovate, allowing her to explore and advance AI-enabled tech
Core technologies and applications
SRI’s Center for Vision Technologies (CVT) tackles data acquisition and exploitation challenges across a broad range of applications and industries. Our researchers work in cross-disciplinary teams, including robotics and artificial intelligence, to advance, combine and customize technologies in areas including computational sensing, 2D-3D reasoning, collaborative autonomy, human behavior modeling, vision analytics, and machine learning.
Recent publications
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Computational sensing and low-power processing
Real-Time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators.
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2d 3d reasoning and augmented reality
Striking the Right Balance: Recall Loss for Semantic Segmentation
Class imbalance is a fundamental problem in computer vision applications such as semantic segmentation.
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Collaborative human-robot autonomy
Graph Mapper: Efficient Visual Navigation by Scene Graph Generation
We propose a method to train an autonomous agent to learn to accumulate a 3D scene graph representation of its […]
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Human behavior modeling
Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction
We present a series of two studies conducted to understand user’s affective states during voice-based human-machine interactions. Emphasis is placed on the cases of communication errors or failures.
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Multi-modal data analytics
Time-Space Processing for Small Ship Detection in SAR
This paper presents a new 3D time-space detector for small ships in single look complex (SLC) synthetic aperture radar (SAR) imagery, optimized for small targets around 5-15 m long that are unfocused due to target motion induced by ocean surface waves.
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Machine learning
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments
We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments. We consider the problem of controlling a sensor that makes partial observations in some space of interest such that it maximizes information about entities present in that space.
Publications
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Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction
We present a series of two studies conducted to understand user’s affective states during voice-based human-machine interactions. Emphasis is placed on the cases of communication errors or failures.
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Incremental Learning with Differentiable Architecture and Forgetting Search
In this paper, we show that leveraging NAS for incremental learning results in strong performance gains for classification tasks.
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Saccade Mechanisms for Image Classification, Object Detection and Tracking
We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed approach is based on the ideas of attention-driven visual processing and saccades, miniature eye movements influenced by attention.
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Conformal Prediction Intervals for Markov Decision Process Trajectories
This paper extends previous work on conformal prediction for functional data and conformalized quantile regression…
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Time-Space Processing for Small Ship Detection in SAR
This paper presents a new 3D time-space detector for small ships in single look complex (SLC) synthetic aperture radar (SAR) imagery, optimized for small targets around 5-15 m long that are unfocused due to target motion induced by ocean surface waves.
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Striking the Right Balance: Recall Loss for Semantic Segmentation
Class imbalance is a fundamental problem in computer vision applications such as semantic segmentation.
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Graph Mapper: Efficient Visual Navigation by Scene Graph Generation
We propose a method to train an autonomous agent to learn to accumulate a 3D scene graph representation of its […]
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SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments.
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Broadening AI Ethics Narratives: An Indic Arts View
In this paper, we investigate uncovering the unique socio-cultural perspectives embedded in human-made art, which in turn, can be valuable in expanding the horizon of AI ethics.
Our team
“SRI offers a unique blend of academia and industry, which offers an opportunity to work on problems that involve research and are practically relevant.”
Karan Sikka
Computer Scientist, Information & Computing Sciences
