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AI enabled Camera at Network edge for next-gen IoT Solutions

In the fast-moving, high-tech market, product developers are looking for building blocks and frameworks to enable niche cutting-edge solutions. Device manufacturers do not want to reinvent the wheel when designing their products. At the same time, they are looking to design products that are affordable, scalable, and compliant with the latest standards.

So far, artificial intelligence (AI) techniques have been deployed in data centers to leverage the available compute power for performing processor-demanding tasks. With cloud adoption, AI has made its way into software and has also moved to the outer edges of networks. To enable quick action and reduce data overload on the cloud, IoT solution providers are deploying AI techniques at endpoints, gateways, and other devices at the point of use. The inference and training of AI algorithms occur at the backend, while the trained models are deployed on the edge nodes.

In the past, AI/ML and computer vision systems were perceived as highly complex and powerful, requiring a significant amount of memory. Deploying these technologies to high-volume, cost-sensitive IoT edge devices was limited due to stringent BOM and cost constraints. However, cloud-based solutions and multi-core low-power SoCs have made it possible. When it comes to vision-based connected solutions, smart cameras are one of the key components. Smart cameras with IP connectivity, advanced data analytics, and artificial intelligence will drive innovations in the Internet of Things (IoT) and its applications.

 

Few Use Cases

  • Vision-based industrial and manufacturing plant solutions for remote monitoring, quality checks, and worker safety.
  • Driver behavior and fleet monitoring solutions for higher efficiency and remote diagnostics.
  • Intelligent checkout solutions for retail stores by identifying cart items; inventory management and monitoring for retail stores.
  • Enterprise automation solutions including A/V collaboration, interactive displays, access control, and surveillance solutions.
  • Smart HMI for kitchen appliances – computer vision for food detection, recognition, and activity tracking; natural language processing for voice assistants to issue commands to appliances.

 

Key Technical Considerations for Purpose Built Cameras for Intelligent IoT Devices

On-device machine learning

Required to perform a variety of video analytics tasks, including object detection, facial detection and recognition, multi-object tracking, and object classification.

Enhanced camera features

Camera with enhanced features such as dual ISPs, the ability to capture and stream premium 4K High Efficiency Video Coding (HEVC), support for various lighting conditions, noise reduction, and machine learning applications.

High Performance for Edge Computing

System-on-a-chip (SoC) solutions with customized CPUs, GPUs, and DSP processing engines.

Combining powerful image processing and AI with machine learning, Qualcomm offers chipsets and software frameworks designed to create a wide variety of intelligent IoT devices for consumer and enterprise applications. These applications include home security, enterprise security, 360 cameras, portable cameras, wearable devices, dash cams, smart displays, and more. The QCS8250 System-on-Chip (SoC) brings the latest technologies in a highly integrated chipset, delivering exceptional performance and features. This chip is purpose-built for enterprise and commercial IoT applications such as video collaboration, smart cameras, connected healthcare, smart retail, and more.

Qualcomm’s QCS8250 application processor offers high-performance computing, along with a dedicated Qualcomm® AI Engine, to efficiently run complex AI and deep learning workloads and perform on-device edge inferencing at incredibly low power. eInfochips has launched a portfolio of modules and kits called ‘Aikri,’ based on the latest Qualcomm processors, including the QCS8250.

The eInfochips Reusable Camera Framework (RCF) is a hardware platform-agnostic solution that can be used as a firmware platform for IP camera design. Its core services drastically reduce development efforts and accelerate time-to-market. It is purpose-built to kick-start camera development, allowing OEMs to focus on developing and rolling out breakthrough technology innovations.

eInfochips, in collaboration with Arrow and Qualcomm, has established “Edge Labs” – a Center of Excellence that provides access to Qualcomm Subject Matter Experts (SMEs), end-to-end product development services, state-of-the-art labs, and equipment to accelerate product development. eInfochips has designed over 30 camera designs, leveraging an in-house state-of-the-art image tuning lab. Additionally, eInfochips possesses experience with the latest AI frameworks and tools such as TensorFlow, OpenCV, Python, Caffe, and Keras.

Picture of Aarohi Desai

Aarohi Desai

Aarohi Desai is a Product and Practice Marketing Manager at eInfochips. She holds a Master's degree in Electrical and Computer Engineering from Georgia Tech and was working with NVIDIA in Silicon Valley before joining eInfochips. Leveraging her technology domain and experience, she is now focusing on enabling embedded solutions based on Qualcomm Snapdragon Platforms at eInfochips.

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