AI-Powered Cameras – Hailo AI Ep 4

AI-powered smart cameras are revolutionizing video analytics and image quality. In this Edge AIsle episode, we examine how Hailo’s AI vision processors, including the Hailo-15 family, are pushing the boundaries of video processing by seamlessly integrating AI into cameras. From enhancing video clarity to running advanced analytics like facial recognition and gesture estimation, Hailo AI enables real-time insights that transform how businesses and public entities monitor and analyze visual data.

With AI now on the edge, cameras no longer rely on cloud processing, reducing latency, improving privacy, and cutting costs. We’ll discuss how Hailo’s AI solutions empower cameras to handle complex tasks like denoising, HDR, and object detection, all while maintaining low power consumption. These advancements make AI-enhanced cameras more scalable and efficient across industries, including security, transportation, and retail.

Tune in as we explore how Hailo’s AI processors elevate video analytics to a new level, enabling smarter decision-making and improving the efficiency of surveillance systems worldwide. Learn how AI-powered cameras are paving the way for a smarter, safer world.

Chapters
(00:00) Welcome to Hailo Audio Blog
(00:13) AI-Powered Smart Cameras Overview
(00:22) Empowering Cameras with AI
(00:48) The Booming Camera Market
(01:18) Enhancing Safety & Security with Smart Cameras
(01:53) Edge AI for Real-Time Analytics
(02:22) Optimizing Data Transfer with Edge AI
(03:03) AI Capacity Demands in Smart Cameras
(03:18) Advanced Image Enhancement Features
(03:42) Image Correction & Stabilization Techniques
(04:11) Noise Removal and Quality Optimization
(04:42) Advanced Video Analytics Pipelines
(05:20) Road Surveillance & License Plate Recognition
(05:44) Advanced Analytics for Behavior Detection
(06:28) Integrating Video Enhancement with Analytics
(07:27) AI TOPs Compute Requirements for Smart Camera Operations
(08:16) Introducing Hailo AI Vision Processors
(08:19) The Hailo 15 Vision Processor Series
(08:34) Hailo 15 Specs for High-End Cameras
(08:49) Superior Efficiency & AI Performance
(09:31) Recap: AI in Smart Cameras
(09:58) The Future Impact of AI-Driven Smart Cameras
(10:09) Conclusion on Hailo AI in Smart Cameras

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What is Hailo AI - The Edge AIsle?

"The Edge AIsle" brings you to the forefront of artificial intelligence and edge computing, powered by Hailo.ai. In this podcast, we explore how edge AI is reshaping industries from smart cities and intelligent video analytics to autonomous vehicles and retail innovation. Join industry experts and thought leaders as they share insights into how Hailo’s AI processors are transforming the way industries function, enabling real-time deep learning and AI-driven applications directly on edge devices.
This isn’t just a podcast about technology—it's about how AI is empowering industries and improving lives across the globe. Whether it’s smart cameras making our cities safer or AI accelerators driving innovation in autonomous vehicles, the possibilities are endless.
If you're passionate about the nuts and bolts of AI processors and how they integrate with edge platforms to deliver unparalleled performance, "The Edge AIsle" is the podcast for you. Expect detailed analysis and a peek behind the curtain at the future of edge computing AI.

Host 1: 00:01

Welcome to another audio blog by Hailo. Bringing you innovations and insights into AI on the edge. Now let's get started.

Host 2: 00:13

Hi. Today, we will discuss AI powered smart cameras and how AI enables transforming vision into insights. As the camera market is booming, so does the need to empower cameras with artificial intelligence. In this audio blog, we will talk about the need for on camera AI, and how it enhances video quality and empowers advanced video analytics. Also, we will talk about some typical cameras and applications and try to estimate the AI budget required to execute the different scenarios.

Host 2: 00:47

In today's tech driven world, cameras have become an integral part of our daily lives and we are used to constantly recording videos and being recorded. The rapid deployment of IP cameras in residential homes, commercial and public space, and the industrial sector is fueling an unprecedented growth in the market, which is estimated by ABI Research to reach 200,000,000 cameras by 2027 with a revenue of $35,000,000,000. The most significant growth driver for this market is the ability to improve safety and optimizing traffic management, smart cameras provide countless benefits and endless opportunities for a smarter and safer world. With the proliferation of camera deployments comes the need to automate and enhance the ability to monitor the video streams and generate insights from them, as well as to make streaming and storage of video more efficient and cost effective. This is where artificial intelligence comes to play.

Host 2: 01:56

Since traditional cloud based AI models often suffer from latency issues, not enabling real time insights and alerts. In addition to posing some privacy concerns and network dependency, the need to enable AI at the edge is surging along with the smart camera market. Edge AI guarantees video analytics, insights, and alerts in real time, enabling a higher level of security. In addition, AI at the edge enables streaming of only metadata and insights from the video, thus reducing the cost of transferring, computing, and storing on the cloud while enhancing people's privacy and eliminating network dependencies. However, most of these IP cameras have a limited amount of compute power.

Host 2: 02:43

What sets apart the next generation of smart cameras is their incorporation of high compute power and AI processing capacity, enabling not only processing of complex and advanced video analytics tasks, but also applying AI for video enhancement to provide top quality video image. As each of these functions, enhancing video quality and enabling advanced video analytics, is demanding its own budget of AI capacity. Today's smart cameras need to be equipped with the right amount of AI power to address the needs. AI can be used to enhance image quality and provide a clear and sharp image, even from a poor quality video. AI can handle a variety of image enhancement tasks such as noise reduction in low light conditions, high dynamic range, or HDR, and even some aspects of the classic three a, which refers to auto exposure, auto white balance, and autofocus.

Host 2: 03:42

AI can correct image distortion, stabilize image, and compensate for motion, and enable digital zoom. Extreme low light conditions, for example, may cause a reduction in viewing distance, poor image quality, and poor color capture. Noise also reduces the ability to differentiate details in the image, resulting in increased data size during compression leading to low efficiency in transmitting and storing video data in the cloud. AI can be leveraged to remove noise while preserving important image details and textures resulting in higher image quality as measured by signal to noise ratio and structural similarity index measure known as SNR and SSIM respectively. As an example, noise removal from a four k image taken at low light conditions of about five lux would require approximately 100 giga ops per frame, which is three tera ops for real time video streaming at 30 FPS.

Host 2: 04:42

As higher resolution video streams become more common, the need arises to process a greater amount of data, detect and identify more complex and granular objects, and execute more tasks and more complex pipelines. When a camera has enough AI capacity, it can support advanced video analytics on top of the AI powered video enhancement. These can include running multiple AI tasks and models with complex pipelines on the same video stream, identifying smaller and more distant objects with higher accuracy and less false alarms, or performing faster detection at high resolution. For example, a road surveillance camera can run a complex pipeline such as automatic license plate recognition, which requires object detection to identify every car on the road, followed by license plate detection to identify the license plate within every car, and finally, license plate recognition to determine the characters in each license plate. With the right amount of compute power, additional tasks and pipelines can be handled by the same camera.

Host 2: 05:48

For example, running a segment anything model to clearly identify objects in the video in higher resolution unless false or missed detection. It can then perform classification to identify abnormal, illegal, or dangerous behavior, such as line crossing, over speeding, dangerous overtaking, driving in the wrong direction, not maintaining enough distance between cars, driving in a non drivable space, etcetera. When such an abnormal behavior is recognized, the license plate of the violating vehicle can be retrieved in order to generate an alert to law enforcement agents. To obtain high accuracy analytics at high image quality, smart cameras need enough AI power to run both video enhancement and analytic tasks in parallel. The ability to intertwine video enhancement and analytics tasks benefits both the visual outcome as well as the analytics insights.

Host 2: 06:45

In order to get an accurate license plate number and person identification, the camera's vision processor needs to apply semantic awareness in order to run denoising techniques selectively based on the semantic significance of elements in the frame and perform video processing differently based region of interest. Noise reduction needs to be applied while preserving important image details and textures for higher signal to noise ratio and structural similarity index measure, and the processing of both noise reduction and object detection need to occur simultaneously for faster input and higher FPS, especially in high resolution video. For basic vision tasks such as denoising, a two megapixel camera would require around 0.5 tera operations per second. And for basic video analytics pipelines, such as object detection or people counting, an additional one tera operations per second is needed. To add advanced video enhancement features, such as HDR or four times upscaling digital zoom, an additional one tera operations per second is needed.

Host 2: 07:55

And for advanced analytics, multistage pipeline with three to five stages, such as license plate recognition or facial recognition, two more tera operations per second will be needed to a total of 4.5 tops. A camera with an eight megapixel sensor or two times four megapixel would need four times the amount of tops respectively. So where does Hailo come into the picture? In the spring of twenty twenty three, Hailo launched a family of powerful AI vision processors. The second generation of Hailo's processors named Hailo 15 includes three variants that were designed to fit every camera type and need.

Host 2: 08:34

For the high end cameras, Hailo is offering the hailo 15 h with 20 tops, which enable running all the basic and advanced video enhancement and analytics pipelines with some tops to spare for future extension of camera capabilities. The hailo 15 m and hailo 15 l provide 11 tops and seven tops respectively and are offered at competitive pricing to match the range of cameras they support. The hailo 15 family of system on a chip can process multiple machine learning models simultaneously at a very low power consumption in accordance with camera design requirements. As the neural network core of the hailo 15 vision processors enables fusing both video enhancement and video analytics tasks to achieve superior processing efficiency. No other vision processor currently offered in the market can support these AI TOPS numbers.

Host 2: 09:31

Let's recap everything we've covered so far. The integration of AI into smart cameras is opening a plethora of opportunities for visual intelligence. From transforming video quality to powering advanced analytics, AI at the edge is revolutionizing industries such as security, industrial automation, retail, and more. As we witness a paradigm shift in the way we capture, process, and interpret visual information, Embracing this AI revolution in smart cameras will undoubtedly reshape additional sectors, improving safety, efficiency, and the overall user experience.

Host 1: 10:10

Thank you for listening to the halo audio blog. If you enjoyed this episode, don't forget to sign up and check out more information at halo.ai. Keep the conversation going by sharing this with your peers and never stop exploring the future of AI.