"Using Drones with Embedded Vision to Safeguard Vineyard Health," a Presentation from Chouette
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For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2017-alliance-vitf-nespoulous For more information about embedded vision, please visit: http://www.embedded-vision.com Charles Nespoulous of Chouette delivers the presentation "Using Drones with Embedded Vision to Safeguard Vineyard Health" at the Embedded Vision Alliance's September 2017 Vision Industry and Technology Forum. In his presentation, Nespoulous covers the following topics: ▪ Viticulture challenges ▪ The precision viticulture solution ▪ Chouette overview ▪ Implementation challenges ▪ Chouette's implementation approach ▪ Business model ▪ Future challenges
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/building-accelerated-gstreamer-applications-for-video-and-audio-ai-a-presentation-from-wave-spectrum/
Abdo Babukr, Accelerated Computing Consultant at Wave Spectrum, presents the “Building Accelerated GStreamer Applications for Video and Audio AI,” tutorial at the May 2023 Embedded Vision Summit.
GStreamer is a popular open-source framework for creating streaming media applications. Developers often use GStreamer to streamline the development of computer vision and audio perception applications. Since perceptual algorithms are often quite demanding in terms of processing performance, in many cases developers need to find ways to accelerate key GStreamer building blocks, taking advantage of specialized features of their target processor or co-processor.
In this talk, Babukr introduces GStreamer and shows how to use it to build computer vision and audio perception applications. He also shows how to create efficient, high-performance GStreamer applications that utilize specialized hardware features.“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/understanding-selecting-and-optimizing-object-detectors-for-edge-applications-a-presentation-from-walmart-global-tech/
Md Nasir Uddin Laskar, Staff Machine Learning Engineer at Walmart Global Tech, presents the “Understanding, Selecting and Optimizing Object Detectors for Edge Applications” tutorial at the May 2023 Embedded Vision Summit.
Object detectors count objects in a scene and determine their precise locations, while also labeling them. Object detection plays a crucial role in many vision applications, from autonomous driving to smart appliances. In many of these applications, it’s necessary or desirable to implement object detection at the edge.
In this presentation, Laskar explores the evolution of object detection algorithms, from traditional approaches to deep learning-based methods and transformer-based architectures. He delves into widely used approaches for object detection, such as two-stage R-CNNs and one-stage YOLO algorithms, and examines their strengths and weaknesses. And he provides guidance on how to evaluate and select an object detector for an edge application.“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...Edge AI and Vision Alliance
This document provides an introduction to LiDAR technology for machine perception. It begins with an overview of LiDAR fundamentals and principles, explaining that LiDAR works similarly to radar but uses laser light instead of radio waves. It then discusses different types of LiDAR sensors and scanning methods, as well as strategies for processing LiDAR point cloud data. The document concludes with examples of common LiDAR representations and neural network architectures used for tasks like object detection from LiDAR point clouds.“Vision-language Representations for Robotics,” a Presentation from the Unive...
“Vision-language Representations for Robotics,” a Presentation from the Unive...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/vision-language-representations-for-robotics-a-presentation-from-the-university-of-pennsylvania/
Dinesh Jayaraman, Assistant Professor at the University of Pennsylvania, presents the “Vision-language Representations for Robotics” tutorial at the May 2023 Embedded Vision Summit.
In what format can an AI system best present what it “sees” in a visual scene to help robots accomplish tasks? This question has been a long-standing challenge for computer scientists and robotics engineers. In this presentation, Jayaraman provides insights into cutting-edge techniques being used to help robots better understand their surroundings, learn new skills with minimal guidance and become more capable of performing complex tasks.
Jayaraman discusses recent advances in unsupervised representation learning and explains how these approaches can be used to build visual representations that are appropriate for a controller that decides how the robot should act. In particular, he presents insights from his research group’s recent work on how to represent the constituent objects and entities in a visual scene, and how to combine vision and language in a way that permits effectively translating language-based task descriptions into images depicting the robot’s goals.“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsightsEdge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/adas-and-av-sensors-whats-winning-and-why-a-presentation-from-techinsights/
Ian Riches, Vice President of the Global Automotive Practice at TechInsights, presents the “ADAS and AV Sensors: What’s Winning and Why?” tutorial at the May 2023 Embedded Vision Summit.
It’s clear that the number of sensors per vehicle—and the sophistication of these sensors—is growing rapidly, largely thanks to increased adoption of advanced safety and driver assistance features. In this presentation, Riches explores likely future demand for automotive radars, cameras and LiDARs.
Riches examines which vehicle features will drive demand out to 2030, how vehicle architecture change is impacting the market and what sorts of compute platforms these sensors will be connected to. Finally, he shares his firm’s vision of what the landscape could look like far beyond 2030, considering scenarios out to 2050 for automated driving and the resulting sensor demand.“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/computer-vision-in-sports-scalable-solutions-for-downmarkets-a-presentation-from-sportlogiq/
Mehrsan Javan, Co-founder and CTO of Sportlogiq, presents the “Computer Vision in Sports: Scalable Solutions for Downmarket Leagues” tutorial at the May 2023 Embedded Vision Summit.
Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this requires a fully automated, robust end-to-end pipeline, spanning from visual input, to player and group activities, to player and team evaluation to planning. Despite major advancements in computer vision and machine learning, today sports analytics solutions are limited to top leagues and are not widely available for downmarket leagues and youth sports.
In this talk, Javan explains how his company has developed scalable and robust computer vision solutions to democratize sport analytics and offer pro-league-level insights to leagues with modest resources, including youth leagues. He highlights key challenges—such as the requirement for low-cost, low-latency processing and the need for robustness despite variations in venues. He discusses the approaches Sportlogiq tried and how it ultimately overcame these challenges, including the use of transformers and fusion of multiple type of data streams to maximize accuracy.“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...
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1) The document proposes a novel statistical method to detect and estimate the magnitude of data drift in image classification neural networks. It considers various types of data drift including noise effects, weather effects, and camera degradation.
2) The method introduces a "threshold probability" for each class to determine the percentage of predictions above the threshold at different drift magnitudes. This allows estimating the drift magnitude based on the difference between expected and observed percentages.
3) Experimental results on MNIST digit classification show the method can accurately detect and estimate the magnitude of different types of data drift like Gaussian noise, Poisson noise, and Salt & Pepper noise. The proposed approach helps neural networks cope with data drifts during operation.“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/deep-neural-network-training-diagnosing-problems-and-implementing-solutions-a-presentation-from-sensor-cortek/
Fahed Hassanat, Chief Operating Officer and Head of Engineering at Sensor Cortek, presents the “Deep Neural Network Training: Diagnosing Problems and Implementing Solutions” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Hassanat delves into some of the most common problems that arise when training deep neural networks. He provides a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score.
Hassanat then explores training challenges that arise from problems with hyperparameters, inappropriately sized models, inadequate models, poor-quality datasets, imbalances within training datasets and mismatches between training and testing datasets. To help detect and diagnose training problems, he also covers techniques such as understanding performance curves, recognizing overfitting and underfitting, analyzing confusion matrices and identifying class interaction issues.
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/ai-start-ups-the-perils-of-fishing-for-whales-war-stories-from-the-entrepreneurial-front-lines-a-presentation-from-seechange-technologies/
Tim Hartley, Vice President of Product for SeeChange Technologies, presents the “AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepreneurial Front Lines)” tutorial at the May 2023 Embedded Vision Summit.
You have a killer idea that will change the world. You’ve thought through product-market fit and differentiation. You have seed funding and a world-beating team. Best of all, you’ve caught the attention of major players in your industry. You’ve reached peak “start-up”—that point of limitless possibility—when you go to bed with the same level of energy and enthusiasm you had when you woke. And then the first proof of concept starts…
In this talk, Hartley lays out some of the pitfalls that await those building the next big thing. Using real examples, he shares some of the dos and don’ts, particularly when dealing with that big potential first customer. Hartley discusses the importance of end-to-end design, ensuring your product solves real-world problems. He explores how far the big companies will tell you to jump—and then jump again—for free. And, most importantly, how to build long-term partnerships with major corporations without relying on over-promising sales pitches.“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...
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Andrew Harris, Spacecraft Systems Engineer at SCOUT Space, presents the “Developing a Computer Vision System for Autonomous Satellite Maneuvering” tutorial at the May 2023 Embedded Vision Summit.
Computer vision systems for mobile autonomous machines experience a wide variety of real-world conditions and inputs that can be challenging to capture accurately in training datasets. Few autonomous systems experience more challenging conditions than those in orbit. In this talk, Harris describes how SCOUT Space has designed and trained satellite vision systems using dynamic and physically informed synthetic image datasets.
Harris describes how his company generates synthetic data for this challenging environment and how it leverages new real-world data to improve our datasets. In particular, he explains how these synthetic datasets account for and can replicate real sources of noise and error in the orbital environment, and how his company supplements them with in-space data from the first SCOUT-Vision system, which has been in orbit since 2021.“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...
“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/bias-in-computer-vision-its-bigger-than-facial-recognition-a-presentation-from-santa-clara-university/
Susan Kennedy, Assistant Professor of Philosophy at Santa Clara University, presents the “Bias in Computer Vision—It’s Bigger Than Facial Recognition!” tutorial at the May 2023 Embedded Vision Summit.
As AI is increasingly integrated into various industries, concerns about its potential to reproduce or exacerbate bias have become widespread. While the use of AI holds the promise of reducing bias, it can also have unintended consequences, particularly in high-stakes computer vision applications such as facial recognition. However, even seemingly low-stakes computer vision applications such as identifying potholes and damaged roads can also present ethical challenges related to bias.
This talk explores how bias in computer vision often poses an ethical challenge, regardless of the stakes involved. Kennedy discusses the limitations of technical solutions aimed at mitigating bias, and why “bias-free” AI may not be achievable. Instead, she focuses on the importance of adopting a “bias-aware” approach to responsible AI design and explores strategies that can be employed to achieve this.“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...
“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...Edge AI and Vision Alliance
The document discusses sensor fusion techniques for accurately perceiving objects in the environment. It describes sensor fusion as combining data from multiple sensors to produce more accurate, reliable and complete information. It covers types of sensors including passive sensors like cameras and active sensors like LiDAR. It also discusses challenges in sensor fusion and algorithms like Kalman and particle filters. Centralized and decentralized fusion architectures are described as well as metrics for evaluating fusion approaches.“Updating the Edge ML Development Process,” a Presentation from Samsara
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/updating-the-edge-ml-development-process-a-presentation-from-samsara/
Jim Steele, Vice President of Embedded Software at Samsara, presents the “Updating the Edge ML Development Process” tutorial at the May 2023 Embedded Vision Summit.
Samsara (NYSE:IOT) is focused on digitizing the world of operations. The company helps customers across many industries—including food and beverage, utilities and energy, field services and government—get information about their physical operations into the cloud, so they can operate more safely, efficiently and sustainably. Samsara’s sensors collect billions of data points per day and on-device processing is instrumental to its success. The company is constantly developing, improving and deploying ML models at the edge.
Samsara has found that the traditional development process—where ML scientists create models and hand them off to firmware engineers for embedded implementation—is slow and often produces difficult-to-resolve differences between the original model and the embedded implementation. In this talk, Steele presents an alternative development process that his company has adopted with good results. In this process, firmware engineers develop a general framework that ML scientists use to design, develop and deploy their models. This enables quick iterations and fewer confounding bugs.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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For decades, the OpenCV software library has been popular for developing computer vision applications. However, developers have found it challenging to create efficient implementations of their OpenCV applications on processors optimized for edge applications, like the Qualcomm Snapdragon family. As part of its comprehensive support for computer vision application developers, Qualcomm provides a variety of tools to enable developers to take full advantage of the heterogeneous computing resources in the Snapdragon processors.
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In this talk, Dr. Sukumar explains and demonstrates how Qualcomm has been successful in deploying large generative AI and multimodal models on the edge for a variety of use cases in consumer and enterprise markets. He examines key challenges that must be overcome before large models at the edge can reach their full commercial potential. He also highlights how Qualcomm is addressing these challenges through upgraded processor hardware, improved developer tools and a comprehensive library of fully optimized AI models in the Qualcomm AI Hub.“Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment,...
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Maurits Kaptein, Chief Data Scientist at Network Optix and Professor at the University of Eindhoven, presents the “Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment” tutorial at the May 2024 Embedded Vision Summit.
The Embedded Vision Summit brings together innovators in silicon, devices, software and applications and empowers them to bring computer vision and perceptual AI into reliable and scalable products. However, integrating recent hardware, software and algorithm innovations into prime-time-ready products is quite challenging. Scaling from a proof of concept—for example, a novel neural network architecture performing a valuable task efficiently on a new piece of silicon—to an AI vision system installed in hundreds of sites requires surmounting myriad hurdles.
First, building on Network Optix’s 14 years of experience, Professor Kaptein details how to overcome the networking, fleet management, visualization and monetization challenges that come with scaling a global vision solution. Second, Kaptein discusses the complexities of making vision AI solutions device-agnostic and remotely manageable, proposing an open standard for AI model deployment to edge devices. The proposed standard aims to simplify market entry for silicon manufacturers and enhance scalability for solution developers. Kaptein outlines the standard’s core components and invites collaborative contributions to drive market expansion.“What’s Next in On-device Generative AI,” a Presentation from Qualcomm
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The generative AI era has begun! Large multimodal models are bringing the power of language understanding to machine perception, and transformer models are expanding to allow machines to understand using multiple types of sensors. This new wave of approaches is poised to revolutionize user experiences, disrupt industries and enable powerful new capabilities. For generative AI to reach its full potential, however, we must deploy it on edge devices, providing improved latency, pervasive interaction and enhanced privacy.
In this talk, Hou shares Qualcomm’s vision of the compelling opportunities enabled by efficient generative AI at the edge. He also identifies the key challenges that the industry must overcome to realize the massive potential of these technologies. And he highlights research and product development work that Qualcomm is doing to lead the way via an end-to-end system approach—including techniques for efficient on-device execution of LLMs, LVMs and LMMs, methods for orchestration of large models at the edge and approaches for adaptation and personalization.
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- 1. EmbeddedVision Alliance –Vision Industry andTechnology Forum 1 Using Drones with EmbeddedVision to SafeguardVineyard Health September 2017
- 3. 792 000 ha 72 000 ha WINE MARKET
- 4. - 30% harvest yields 500-700 €/ha Cost breakdown of a winery Anti-mildew treatments 420 M€/an in France Up to 100% of harvest damaged Mildew Powdery Mildew Pruning Rising Degraded wine quality Non appreciated wine PROBLEM: GRAPEVINE DISEASES
- 5. 2015 2020 2025 -25% -25% 2030 - ? % PROBLEM: MORE AND MORE SEVERE PLANT PROTECTION PRODUCT REGULATION
- 7. SOLUTION: PRECISION VITICULTURE Automatic precision treatment systems using RTK available BUT NO AUTOMATIC VINEYARD DISEASE DETECTION AND VISUALISATION SERVICE AVAILABLE …. UNTIL CHOUETTE
- 8. CHOUETTE IN FEW WORDS 8
- 9. 9
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- 11. Deep Neural Network Complex image analysis KEY TECHNOLOGIES ARTIFICIAL INTELLIGENCE • Caffe Framework • Fully Convolutional Neural Network • Based on GoogleNet Inception Modules
- 12. CHOUETTE DEPLOYMENT IN 2017 Bordeaux Cognac Champagne Provence Loire
- 13. CHALLENGES 13
- 14. CHALLENGES Massive data processing Low-cost hardware Fast results Ergonomic visualization platform
- 15. HOW DID WE CRACK THE CASE 15
- 16. CHOUETTE APPROACH Deep learning algorithms Automatic data gathering & analysis pipeline Elastic architecture using Amazon EC2 Drone & camera developed using low cost hardware from mobile industry
- 18. Drone purchase 2-4 k€ Drone and multispectral camera Hybrid revenu model Drone maintenance fee 200 €/year
- 19. Hybrid revenu model Annual platform subscription 10-20 €/ha/month Artificial Intelligence Web Platform App Database
- 21. NEXT CHALLENGES Increasing flight coverage ➢ High speed camera ➢ High resolution camera ➢ Low cost (Near) real time analysis ➢ Embedded deep learning processing chips ➢ Light deep learning algorithms
- 22. 22