Object Identification Functions
Face Mask Detection in Street Camera Video Streams Using AI: Behind the Curtain
This article was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. In the new world of coronavirus, multidisciplinary efforts have been organized to slow the spread of the pandemic. The AI community has also been a part of these endeavors. In particular, developments for… Face Mask Detection in Street
Object Recognition: 3 Things You Need to Know
This article was originally published at MathWorks’ website. It is reprinted here with the permission of MathWorks. What Is Object Recognition? Object recognition is a computer vision technique for identifying objects in images or videos. Object recognition is a key output of deep learning and machine learning algorithms. When humans look at a photograph or
What Is Object Detection?
This article was originally published at MathWorks’ website. It is reprinted here with the permission of MathWorks. 3 Things You Need to Know Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. When humans
“Object Detection for Embedded Markets,” a Presentation from Imagination Technologies
Paul Brasnett, PowerVR Business Development Director for Vision and AI at Imagination Technologies, presents the “Object Detection for Embedded Markets” tutorial at the May 2019 Embedded Vision Summit. While image classification was the breakthrough use case for deep learning-based computer vision, today it has a limited number of real-world applications. In contrast, object detection is
“Sensory Fusion for Scalable Indoor Navigation,” a Presentation from Brain Corp
Oleg Sinyavskiy, Director of Research and Development at Brain Corp, presents the “Sensory Fusion for Scalable Indoor Navigation” tutorial at the May 2019 Embedded Vision Summit. Indoor autonomous navigation requires using a variety of sensors in different modalities. Merging together RGB, depth, lidar and odometry data streams to achieve autonomous operation requires a fusion of
“Enabling the Next Kitchen Experience Through Embedded Vision,” a Presentation from Whirlpool
Sugosh Venkataraman, Vice President of Technology at Whirlpool, presents the “Enabling the Next Kitchen Experience Through Embedded Vision,” tutorial at the May 2019 Embedded Vision Summit. Our kitchens are the hubs where we spend quality time with family and friends, preparing and eating meals. Today, instructions for cooking a particular meal are just a few
“Teaching Machines to See, Understand, Describe and Predict Sports Games in Real Time,” a Presentation from Sportlogiq
Mehrsan Javan, CTO of Sportlogiq, presents the “Teaching Machines to See, Understand, Describe and Predict Sports Games in Real Time” tutorial at the May 2019 Embedded Vision Summit. Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this means designing a fully-automated, robust, end-to-end pipeline; from visual input,
“Addressing Corner Cases in Embedded Computer Vision Applications,” a Presentation from Netradyne
David Julian, CTO and Founder of Netradyne, presents the “Addressing Corner Cases in Embedded Computer Vision Applications” tutorial at the May 2019 Embedded Vision Summit. Many embedded vision applications require solutions that are robust in the face of very diverse real-world inputs. For example, in automotive applications, vision-based safety systems may encounter unusual configurations of
“Visual AI Enables Autonomous Security,” an Interview with Knightscope
William “Bill” Santana Li, Co-founder, Chairman and CEO of Knightscope, talks with Vin Ratford, Executive Director of the Embedded Vision Alliance, for the “Visual AI Enables Autonomous Security” interview at the May 2019 Embedded Vision Summit. Knightscope, a physical security technologies company based in Silicon Valley, develops and sells a line of autonomous robots that
May 2019 Embedded Vision Summit Slides
The Embedded Vision Summit was held on May 20-23, 2019 in Santa Clara, California, as an educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The presentations delivered at the Summit are listed below. All of the slides from these presentations are included in… May 2019 Embedded Vision Summit
“Recognizing Novel Objects in Novel Surroundings with Single-shot Detectors,” a Presentation from the University of North Carolina at Chapel Hill
Alexander C Berg, Associate Professor at the University of North Carolina at Chapel Hill and CTO of Shopagon, presents the “Recognizing Novel Objects in Novel Surroundings with Single-shot Detectors” tutorial at the May 2018 Embedded Vision Summit. Berg’s group’s 2016 work on single-shot object detection (SSD) reduced the computation cost for accurate detection of object
“Words, Pictures, and Common Sense: Visual Question Answering,” a Presentation from Facebook and Georgia Tech
Devi Parikh, Research Scientist at Facebook AI Research (FAIR) and Assistant Professor at Georgia Tech, presents the “Words, Pictures, and Common Sense: Visual Question Answering” tutorial at the May 2018 Embedded Vision Summit. Wouldn’t it be nice if machines could understand content in images and communicate this understanding as effectively as humans? Such technology would
“Generative Sensing: Reliable Recognition from Unreliable Sensor Data,” a Presentation from Arizona State University
Lina Karam, Professor and Computer Engineering Director at Arizona State University, presents the “Generative Sensing: Reliable Recognition from Unreliable Sensor Data” tutorial at the May 2018 Embedded Vision Summit. While deep neural networks (DNNs) perform on par with – or better than – humans on pristine high-resolution images, DNN performance is significantly worse than human
May 2018 Embedded Vision Summit Slides
The Embedded Vision Summit was held on May 21-24, 2018 in Santa Clara, California, as an educational forum for product creators interested in incorporating visual intelligence into electronic systems and software. The presentations delivered at the Summit are listed below. All of the slides from these presentations are included in… May 2018 Embedded Vision Summit
“A New Approach to Mass Transit Security,” a Presentation from Lux Research
Mark Bünger, Vice President of Research at Lux Research, delivers the presentation "A New Approach to Mass Transit Security" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. Bünger presents a revolutionary computer-vision-based methodology for public transit safety.
“Instrumenting Greenhouses as Data-driven Manufacturing Facilities,” a Presentation from IUNU
Matt King, Chief Technology Officer at IUNU, delivers the presentation "Instrumenting Greenhouses as Data-driven Manufacturing Facilities" at the Embedded Vision Alliance's March 2018 Vision Industry and Technology Forum. King explains how his company is enabling increased efficiency in commercial greenhouses using robotic cameras, computer vision and machine learning.
Data Sets for Machine Learning Model Training
Deep learning and other machine learning techniques have rapidly become a transformative force in computer vision. Compared to conventional computer vision techniques, machine learning algorithms deliver superior results on functions such as recognizing objects, localizing objects within a frame, and determining which pixels belong to which object. Even problems like optical flow and stereo correspondence,
“Using Computer Vision and Machine Learning to Understand Pet Behavior,” a Presentation from PetCube
Alex Neskin, founder and CTO of PetCube, delivers the presentation "Using Computer Vision and Machine Learning to Understand Pet Behavior" at the Embedded Vision Alliance's December 2017 Vision Industry and Technology Forum. Neskin explains how his start-up is using vision and AI to improve the lives of pets and their owners.
