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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CVPR 2023: A comprehensive tour and recent advancements toward real-world visual geo-localization
A synopsis of upcoming tutorials for the CVPR 2023 conference.
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SRI’s Center for Vision Technologies is working to make social media more civil
With funding from DARPA, researchers are building an AI technology designed to work alongside humans to promote more prosocial online behavior.
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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 […]
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 by research area
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Computational sensing and low-power processing
Low-Power In-Pixel Computing with Current-Modulated Switched Capacitors
We present a scalable in-pixel processing architecture that can reduce the data throughput by 10X and consume less than 30 mW per megapixel at the imager frontend.
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2d 3d reasoning and augmented reality
On auxiliary latitudes
The auxiliary latitudes are essential tools in cartography. This paper summarizes methods for converting between them with an emphasis on providing full double-precision accuracy.
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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 environment by simultaneously learning to navigate through said environment.
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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.
Publications
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On auxiliary latitudes
The auxiliary latitudes are essential tools in cartography. This paper summarizes methods for converting between them with an emphasis on providing full double-precision accuracy.
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Low-Power In-Pixel Computing with Current-Modulated Switched Capacitors
We present a scalable in-pixel processing architecture that can reduce the data throughput by 10X and consume less than 30 mW per megapixel at the imager frontend.
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Unpacking Large Language Models with Conceptual Consistency
We propose conceptual consistency to measure a LLM’s understanding of relevant concepts. This novel metric measures how well a model can be characterized by finding out how consistent its responses to queries about conceptually relevant background knowledge are.
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Sensor Trajectory Estimation by Triangulating Lidar Returns
The paper describes how to recover the sensor trajectory for an aerial lidar collect using the data for multiple-return lidar pulses.
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Learning with Local Gradients at the Edge
To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient Descent (tpSGD).
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Geodesics on an Arbitrary Ellipsoid of Revolution
The present paper seeks to extend the treatment to arbitrary ellipsoid (in this paper, the term “ellipsoid” should be understood to mean “ellipsoid of revolution”).
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Augmented Reality for Marine Fire Support Team Training
To provide FiSTs with the “sets and reps” required to develop and maintain proficiency, the Office of Naval Research 3D Warfighter Augmented Reality (3D WAR) program is developing an affordable augmented reality (AR) field simulator.
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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.
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