“A Mask Detection Smart Camera Using the Nvidia Jetson Nano: System Architecture and Developer Experience,” a Presentation from BDTI and Tryolabs
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/07/a-mask-detection-smart-camera-using-the-nvidia-jetson-nano-system-architecture-and-developer-experience-a-presentation-from-bdti-and-tryolabs/ Evan Juras, Computer Vision Engineer at BDTI, and Braulio Ríos, Machine Learning Engineer at Tryolabs, co-present the “A Mask Detection Smart Camera Using the Nvidia Jetson Nano: System Architecture and Developer Experience” tutorial at the May 2021 Embedded Vision Summit. MaskCam is a prototype reference design for a smart camera that counts the number of people wearing masks in its field of view and reports statistics to the cloud. Based on the Nvidia Jetson Nano, all AI processing is handled on the device. In this presentation, BDTI and Tryolabs share their story of developing MaskCam, and how the two companies went from concept to working prototype with a small team in just a few months. Juras and Ríos explain the hardware and software design of the camera and present their companies’ experiences working with Jetson Nano (both the Dev Kit and the system-on-module), NVIDIA’s DeepStream framework and containerization via balenaOS/balenaCloud. The solution features a custom tracking component, static video file serving, video streaming over RTSP, and MQTT communication to report statistics and receive commands using a web interface. Working with Jabil Optics, Juras and Ríos also present estimated manufacturing costs for volume production.
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This document describes a robotics and deep learning internship program focused on providing engineers with the latest industry tools to train, optimize, and deploy models on embedded and cloud platforms. The program will involve working with cloud GPU instances, containers, IoT platforms, and tools like OpenCV, Numpy, Pandas, Linux commands, and Python programming. Interns will learn about image and video formats, streaming protocols, AWS, Nvidia SDKs, and will choose to specialize in topics like video analytics, robotics/autonomous cars, or MLOps. Requirements include an engineering degree and experience with deep learning courses, Python, OOP, data cleaning, and network programming.
AI on the Edge
The next evolution in cloud computing is a smarter application not in the cloud. As the cloud has continued to evolve, the applications that utilize it have had more and more capabilities of the cloud. This presentation will show how to push logic and machine learning from the cloud to an edge application. Afterward, creating edge applications which utilize the intelligence of the cloud should become effortless.
Forecast 2012 Panel: Service Provider Innovation Richard Villars, IDC
http://www.opendatacenteralliance.org/
Moderator:
Richard Villars,
Vice President, Information & Cloud
IDC
Panelists:
Curt Aubley,
VP & CTO Cyber Security & NexGen
Innovation
Lockheed Martin
Jeffrey R Deacon,
Chief Cloud Strategist,
Terremark
Joe Weinman,
Senior Vice President, Cloud Services and Strategy
Telx
Michael Kollar,
Global Cloud CTO, ATOS
Petteri Uljas,
Capgemini Finland, CEO
Capgemini Infra3 (Eastern Europe and India), Unit Head Capgemini
Nex optic presentation-edits_8244_final
NexOptic is a technology company that has developed an AI solution called Aliis that can enhance images and videos in real-time. Aliis uses patented neural network technology to transform images on a pixel-by-pixel basis to provide significant data compression and storage savings while also improving image quality. Aliis can be deployed on the edge, in the cloud, or anywhere in between. NexOptic seeks to license or provide subscriptions to Aliis to applications in industries such as mobile, security, automotive, and healthcare that could benefit from Aliis' capabilities.
[Skolkovo Robotics V] Race for AI: What do VCs expect from AI startups?
VCs expect AI startups to focus on developing AI as a product to solve real-world problems, rather than focusing only on the complex technology. They also expect startups to have a clear strategy for acquiring high-quality data, as access to data is often more important than algorithmic advances alone. Rapid growth is important, but startups need to prioritize developing solutions for specific markets and having the right product over raw technology or speed of growth.
"Combining Cloud and Edge Machine Learning to Deliver the Future of Video Mon...
"Combining Cloud and Edge Machine Learning to Deliver the Future of Video Mon...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/feb-2017-member-meeting-camio
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Carter Maslan and Luca de Alfaro of Camio deliver the presentation "Combining Cloud and Edge Machine Learning to Deliver the Future of Video Monitoring" at the February 2017 Embedded Vision Alliance Member Meeting. Maslan and de Alfaro present their company's approach to using machine learning at the edge and in the cloud to deliver more effective video monitoring for consumers.Human Alert Sensor Design
The document describes the design of a human alert sensor device using an XENO+ Nano ML module. The sensor aims to detect 11 types of sounds including baby crying, glass breaking, and screaming. It will load an ML model onto the module's neural decision processor to classify audio clips. Upon detecting an event, it will send a notification to a cloud server via WiFi. The document outlines the target sounds, module specifications, model training process using the Syntiant toolkit, and integrating trained models onto the module for real-time sound classification.
Human Alert Sensor
The document describes the design of a human alert sensor device using an XENO+ Nano ML module. The goal is to load an ML model onto the module that can detect 11 types of sounds including baby crying, glass breaking, and screaming. It provides details on the module components, the training process using audio datasets, and loading the trained model onto the module to classify sounds in real-time. Key steps include collecting audio data, preprocessing the data, training a model using TDK software, and generating model packages that can be downloaded and run on the XENO+ module for sound classification.
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David Davis, Senior Embedded Software Engineer, and John Withers, Automation and Systems Engineer, both of BDTI, present the “TensorFlow Lite for Microcontrollers (TFLM): Recent Developments” tutorial at the May 2022 Embedded Vision Summit.
TensorFlow Lite Micro (TFLM) is a generic inference framework designed to run TensorFlow models on digital signal processors (DSPs), microcontrollers and other embedded targets with small memory footprints and very low power usage. TFLM aims to be easily portable to various embedded targets from those running an RTOS to bare-metal code. TFLM leverages the model optimization tools from the TensorFlow ecosystem and has additional embedded-specific optimizations to reduce the memory footprint. TFLM also integrates with a number of community contributed optimized hardware-specific kernel implementations.
In this talk, Davis and Withers review collaboration between BDTI and Google over the last year, including porting nearly two dozen operators from TensorFlow Lite to TFLM, creation of a separate Arduino examples repository, improved testing and documentation of both Arduino and Colab training examples and transitioning TFLM’s open-source CI framework to use GitHub Actions.Mitchell Reifel (pmdtechnologies ag): pmd Time-of-Flight – the Swiss Army Kni...
The document discusses 3D imaging technologies and their applications. It provides an overview of different 3D sensing techniques such as stereo vision, structured light, and time-of-flight. Time-of-flight allows for compact solutions and flexible modes. The document outlines the growth of 3D markets from mobile to augmented reality. It argues that time-of-flight will be important for applications like mobile AR as it enables gesture control through depth sensing.
Report
This document provides an overview of an IoT-based wireless electronic notice board project. The system will allow users to send notices from an Android phone to be displayed on an LCD screen notice board. Key aspects include:
- The main controlling device is a Raspberry Pi processor connected to a WiFi modem and LCD display. Notices sent from an Android app will be received by the WiFi modem and processed/displayed by the Raspberry Pi.
- The objectives are to develop a wireless notice board that can display notices sent from a mobile phone in a timely manner, as well as support sending documents, images, and videos.
- The existing manual notice boards have limitations like being time-consuming to maintain
Paper discussion:Video-to-Video Synthesis (NIPS 2018)
This presentation was used in Ridge-i Yomekai event in decemver 2018 for a NIPS2018 paper named Video-to-Video Synthesis delivered by researchers from Nvidia and MIT.
“10 Things You Must Know Before Designing Your Own Camera,” a Presentation fr...
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/10-things-you-must-know-before-designing-your-own-camera-a-presentation-from-panopteo/
Alex Fink, consultant at Panopteo, presents the “10 Things You Must Know Before Designing Your Own Camera” tutorial at the May 2021 Embedded Vision Summit.
Computer vision requires vision. This is why companies that use computer vision often decide they need to create a custom camera module (and perhaps other custom sensors) that meets the specific needs of their unique application. This presentation will help you understand how cameras are different from other types of electronic products; what mistakes companies often make when attempting to design their own cameras; and what you can do to end up with cameras that are built on spec, on schedule and on budget.NTT Docomo's Challenge looking ahead the world pf 5G × OpenStack - OpenStack最...
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タイトル：NTT Docomo's Challenge looking ahead the world pf 5G × OpenStack
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- Current Challenge
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-- BizDevOps
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-- Next Challenge"From 2D to 3D: How Depth Sensing Will Shape the Future of Vision," a Present...
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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/may-2018-embedded-vision-summit-girardin
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Guillaume Girardin, Division Director for photonics, sensing and displays at Yole Développement, presents the "From 2D to 3D: How Depth Sensing Will Shape the Future of Vision" tutorial at the May 2018 Embedded Vision Summit.
For several decades, 3D imaging and sensing technologies have matured, thanks to extensive, successful deployments in high-end applications, mainly in medical and industrial markets. More recently, as 3D sensor costs have dropped, 3D imaging and sensing devices have become a significant business, generating almost $2B in revenue in 2017, with strong growth prospects driven by consumer devices such as the iPhone X with its TrueDepth camera module.
Currently there are three main depth sensing approaches on the market: stereo vision, structured light and time-of-flight. Each of these approaches has pros and cons, from a technical point of view and with respect to cost. Girardin explains the key strengths and weaknesses of each of these technologies and analyzes the cost of adding them to a system based on his firm's recent teardown analyses. He also gives an overview of this depth sensing sensor market and the main factors driving it, and explores how this market could shape the future of vision.Mass fever scanning solution
Your Ideal Mass Fever / Thermal Scan Solution:
High Accuracy; Non-Contact Measurement; No more queuing, Low Cost
Iaetsd arm based remote surveillance and motion detection
This document describes an arm-based remote surveillance and motion detection system using MJPEG compression. The system uses an ARM9 processor and Linux operating system to capture video from a camera. The video is compressed using MJPEG and transmitted over the internet. Users can view the live video stream and detect motions using a web browser. The system is designed for applications like security, transportation and home monitoring due to its low cost, stability and security compared to traditional DSP-based solutions.
JUNIPER: Towards Modeling Approach Enabling Efficient Platform for Heterogene...
JUNIPER: Towards Modeling Approach Enabling Efficient
Platform for Heterogeneous Big Data Analysis
Paper presented at CEE SECR 2014
Similar to “A Mask Detection Smart Camera Using the Nvidia Jetson Nano: System Architecture and Developer Experience,” a Presentation from BDTI and Tryolabs (20)
“Five Things You Might Overlook on Your Next Vision-enabled Product Design,” ...
“Five Things You Might Overlook on Your Next Vision-enabled Product Design,” ...
Quebec - 16 November 2022 - Canada CNCF Meetups.pdf
Quebec - 16 November 2022 - Canada CNCF Meetups.pdf
“What’s Next in On-device Generative AI,” a Presentation from Qualcomm
“What’s Next in On-device Generative AI,” a Presentation from Qualcomm
Implementation Of Video Digital Watermarking Based on Python
Implementation Of Video Digital Watermarking Based on Python
“TensorFlow Lite for Microcontrollers (TFLM): Recent Developments,” a Present...
“TensorFlow Lite for Microcontrollers (TFLM): Recent Developments,” a Present...
Mitchell Reifel (pmdtechnologies ag): pmd Time-of-Flight – the Swiss Army Kni...
Mitchell Reifel (pmdtechnologies ag): pmd Time-of-Flight – the Swiss Army Kni...
Paper discussion:Video-to-Video Synthesis (NIPS 2018)
Paper discussion:Video-to-Video Synthesis (NIPS 2018)
“10 Things You Must Know Before Designing Your Own Camera,” a Presentation fr...
“10 Things You Must Know Before Designing Your Own Camera,” a Presentation fr...
NTT Docomo's Challenge looking ahead the world pf 5G × OpenStack - OpenStack最...
NTT Docomo's Challenge looking ahead the world pf 5G × OpenStack - OpenStack最...
"From 2D to 3D: How Depth Sensing Will Shape the Future of Vision," a Present...
"From 2D to 3D: How Depth Sensing Will Shape the Future of Vision," a Present...
Iaetsd arm based remote surveillance and motion detection
Iaetsd arm based remote surveillance and motion detection
JUNIPER: Towards Modeling Approach Enabling Efficient Platform for Heterogene...
JUNIPER: Towards Modeling Approach Enabling Efficient Platform for Heterogene...
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Amol Borkar, Product Marketing Director at Cadence, presents the “Addressing Tomorrow’s Sensor Fusion and Processing Needs with Cadence’s Newest Processors” tutorial at the May 2024 Embedded Vision Summit.
From ADAS to autonomous vehicles to smartphones, the number and variety of sensors used in edge devices is increasing: radar, LiDAR, time-of-flight sensors and multiple cameras are more and more common. And, as sensors have improved, the data rates associated with them have also increased. Traditionally, a dedicated processor has been utilized to process data from each sensor independently. Today, however, there is a growing need for a single, unified processor capable of processing multimodal sensor data utilizing both classical and AI algorithms and implementing sensor fusion for robust perception.
In this talk, Borkar introduces the new Vision 341 DSP and Vision 331 DSP from Cadence. These cores provide a versatile single-DSP solution for various workloads, including image sensing, radar, LiDAR and AI tasks.He explores the architecture of these new processors, highlights their performance and efficiency and outlines the associated developer tools and software building blocks.“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
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Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.“Silicon Slip-Ups: The Ten Most Common Errors Processor Suppliers Make (Numbe...
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Phil Lapsley, Co-founder and Vice President of BDTI, presents the “Silicon Slip-Ups: The Ten Most Common Errors Processor Suppliers Make (Number Four Will Amaze You!)” tutorial at the May 2024 Embedded Vision Summit.
For over 30 years, BDTI has provided engineering, evaluation and advisory services to processor suppliers and companies that use processors in products. The company has seen a lot, including some classic mistakes. (You know, things like: the chip has an accelerator, but no easy way to program it… or you can only program it using an obscure proprietary framework. Or it has an ISP that only works with one image sensor. Or the development tools promise a lot but fall far short. Or the device drivers don’t work. Or the documentation is deficient.)
Phil Lapsley, BDTI co-founder, presents a fun and fast-paced review of some of the most common processor provider errors, ones seen repeatedly at BDTI. If you’re a processor provider, you’ll learn things you can do to avoid these goofs—and if you’re a processor user, you’ll learn about things to watch for when selecting your next processor!“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
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Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.“How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge,...
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Stephen Su, Senior Segment Marketing Manager at Arm, presents the “How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge has been transforming over the last few years, with newer use cases running more efficiently and securely. Most edge AI workloads were initially run on CPUs, but machine learning accelerators have gradually been integrated into SoCs, providing more efficient solutions. At the same time, ChatGPT has driven a sudden surge in interest in transformer-based models, which are primarily deployed using cloud resources. Soon, many transformer-based models will be modified to run effectively on edge devices.
In this presentation, Su explains the role of transformer-based models in vision applications and the challenges of implementing transformer models at the edge. Next, he introduces the latest Arm machine learning solution and how it enables the deployment of transformer-based vision networks at the edge. Finally, he shares an example implementation of a transformer-based embedded vision use case and uses this to contrast such solutions with those based on traditional CNN networks.“Nx EVOS: A New Enterprise Operating System for Video and Visual AI,” a Prese...
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Nathan Wheeler, Co-founder and CEO of Network Optix, presents the “Nx EVOS: A New Enterprise Operating System for Video and Visual AI” tutorial at the May 2024 Embedded Vision Summit.
In most software domains, developers don’t write code at the bare-metal level; they build applications on top of operating systems, which provide commonly needed functionality. Yet, today, developers of video and AI applications are effectively writing their applications at the bare-metal level, building the “plumbing” themselves to handle basics like device discovery, storage management, security and model deployment. These developers need an operating system that supports their applications so they can focus on what really matters: the core functionality of their product.
Nx EVOS is the world’s first enterprise video operating system. EVOS revolutionizes video management, offering device discovery, bandwidth optimization and security features—in cloud and on device. Its support for AI pipelines and user management enables scalable deployment of AI applications across environments and platforms, and it’s trusted by leading organizations such as SpaceX. In this presentation, you’ll learn how Nx EVOS can save you time and effort in building your next vision product.“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation."OpenCV for High-performance, Low-power Vision Applications on Snapdragon," a...
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Xin Zhong, Computer Vision Product Manager at Qualcomm Technologies, presents the “OpenCV for High-performance, Low-power Vision Applications on Snapdragon” tutorial at the May 2024 Embedded Vision Summit.
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.
In this talk, Zhong introduces a new element of Qualcomm’s computer vision tools suite: a version of OpenCV optimized for Snapdragon platforms, which allows developers to leverage and port their existing OpenCV-based applications seamlessly to Snapdragon platforms. Supporting OpenCV v4.x and later releases, this implementation contains unique Qualcomm-specific accelerations of OpenCV and OpenCV extension APIs. Zhong explains how this library enables developers to leverage existing OpenCV code to achieve superior performance and power savings on Snapdragon platforms.“Deploying Large Models on the Edge: Success Stories and Challenges,” a Prese...
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Vinesh Sukumar, Senior Director of Product Management at Qualcomm Technologies, presents the “Deploying Large Models on the Edge: Success Stories and Challenges” tutorial at the May 2024 Embedded Vision Summit.
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.“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...
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Shuvra Bhattacharyya, Professor at the University of Maryland at College Park, presents the “Learning Compact DNN Models for Embedded Vision” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Bhattacharyya explores methods to transform large deep neural network (DNN) models into effective compact models. The transformation process that he focuses on—from large to compact DNN form—is referred to as pruning. Pruning involves the removal of neurons or parameters from a neural network. When performed strategically, pruning can lead to significant reductions in computational complexity without significant degradation in accuracy. It is sometimes even possible to increase accuracy through pruning.
Pruning provides a general approach for facilitating real-time inference in resource-constrained embedded computer vision systems. Bhattacharyya provides an overview of important aspects to consider when applying or developing a DNN pruning method and presents details on a recently introduced pruning method called NeuroGRS. NeuroGRS considers structures and trained weights jointly throughout the pruning process and can result in significantly more compact models compared to other pruning methods.“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...
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Independent consultant Mohammad Haghighat presents the “Introduction to Computer Vision with Convolutional Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
This presentation covers the basics of computer vision using convolutional neural networks. Haghighat begins by introducing some important conventional computer vision techniques and then transition to explaining the basics of machine learning and convolutional neural networks (CNNs) and showing how CNNs are used in visual perception.
Haghighat illustrates the building blocks and computational elements of neural networks through examples. This session provides an overview of how modern computer vision algorithms are designed, trained and used in real-world applications.“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...
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Parshad Patel, Data Scientist at Yummly, presents the “Selecting Tools for Developing, Monitoring and Maintaining ML Models” tutorial at the May 2023 Embedded Vision Summit.
With the boom in tools for developing, monitoring and maintaining ML models, data science teams have many options to choose from. Proprietary tools provided by cloud service providers are enticing, but teams may fear being locked in—and may worry that these tools are too costly or missing important features when compared with alternatives from specialized providers.
Fortunately, most proprietary, fee-based tools have an open-source component that can be integrated into a home-grown solution at low cost. This can be a good starting point, enabling teams to get started with modern tools without making big investments and leaving the door open to evolve tool selection over time. In this talk, Patel presents a step-by-step process for creating an MLOps tool set that enables you to deliver maximum value as a data scientist. He shares how Yummly built pipelines for model development and put them into production using open-source projects.“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...
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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...
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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...
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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...
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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
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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...
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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.
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- 1. © 2021 BDTI and Tryolabs MaskCam: A Jetson Nano AIoT Mask Detection Camera Evan Juras Braulio Ríos BDTI Tryolabs
- 2. © 2021 BDTI and Tryolabs Agenda 2 About us MaskCam, an AI-enabled smart camera based around Jetson Nano Software design and containerization Hardware design and high volume productization Conclusions and resources
- 3. © 2021 BDTI and Tryolabs About us 3
- 4. © 2021 BDTI and Tryolabs MaskCam: A mask detection smart camera COVID-19 creates a need to monitor crowd size and face mask usage in public areas MaskCam provides mask-wearing statistics in an indoor or outdoor area Reference design for AI-enabled embedded vision applications 4
- 5. © 2021 BDTI and Tryolabs MaskCam in action! 5 https://youtu.be/VofHurI7EZE
- 6. © 2021 BDTI and Tryolabs Original camera video: courtesy of EarthCam Pose Estimation + Classification Object Detection Object classes: mask, no_mask, not_visible Tracking (Norfair) Software: AI model selection
- 7. © 2021 BDTI and Tryolabs FP-16 Software: Optimized object detection
- 8. © 2021 BDTI and Tryolabs Hardware acceleration & parallelization Why DeepStream Pipeline? Maximum performance (inference bottleneck) Fast development (Python) + high performance (C, CUDA) Software: Video pipeline
- 9. © 2021 BDTI and Tryolabs ● Web server • Postgres DB, FastAPI backend, Streamlit frontend ● Remote communication • MQTT protocol: send statistics, receive commands ● Video streaming, save video snapshots • Python multiprocessing • UDP video packets for internal sharing Other Requirements
- 10. © 2021 BDTI and Tryolabs Why containers? Thin layer update for source code changes Dependencies bundled together Better reproducibility Hardware decoupling Easy over-the-air updates Easily link to balenaCloud Containerization
- 11. MaskCam Hardware and Productization
- 12. © 2021 BDTI and Tryolabs Hardware: Design 12 Carrier Board Camera Module Connectivity Module (WiFi or 4G LTE) Jetson Nano SOM Primary components ● Jetson Nano SOM ● Carrier board ● Camera module ● Connectivity module NVIDIA resources ● Jetson Partner Hardware Products list ● Jetson Partner Supported Cameras list ● elinux.org/Jetson_Nano
- 13. © 2021 BDTI and Tryolabs Hardware: Thermal testing 13 Heatsink Final CPU Temp Final GPU Temp Average Inference FPS Passive 97°C 93°C 13.1 Active 48°C 45°C 14.2 Conclusion: final production design requires active heatsink No fan: thermal throttling causes Degraded performance Active fan: stable temperature, maximum performance
- 14. © 2021 BDTI and Tryolabs From prototype to high-volume production 14 Developer Kit Off-the-shelf prototype kit Production model Worked with Jabil to determine next steps for turning MaskCam into a high- volume product and get a preliminary cost estimate
- 15. © 2021 BDTI and Tryolabs Estimated BOM and production cost 15 Item Cost (10K/year, USD) Cost (100K/year, USD) Jetson Nano $99.00 $89.00 Camera Module $42.36 $33.84 Carrier Board $54.82 $53.13 Enclosure, Fan $41.20 $35.29 Total BOM Cost $237.38 $211.26 Production Cost (MVA) $118.69 $84.51 Overall Cost $356.07 $295.77
- 16. © 2021 BDTI and Tryolabs MaskCam is open source! 16 github.com/bdtinc/maskcam (MIT License) Step 1. Pull the Docker container Step 2. Run MaskCam nano@nano-desktop:~$ sudo docker pull maskcam/maskcam-beta nano@nano-desktop:~$ sudo docker run --runtime nvidia --privileged --rm -it -- env MASKCAM_DEVICE_ADDRESS=<your-jetson-ip> -p 1883:1883 -p 8080:8080 -p 8554:8554 maskcam/maskcam-beta
- 17. © 2021 BDTI and Tryolabs Available resources 17 Us! bdti.com tryolabs.com Independent report bdti.com/maskcam GitHub repository github.com/bdtinc/maskcam Come see our demos and talk to us live!
- 18. © 2021 BDTI and Tryolabs 18 Questions? Email us: maskcam@bdti.com