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"The Edge AI-sle" 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 AI-sle" is the podcast for you. Expect detailed analysis and a peek behind the curtain at the future of edge computing AI.
Welcome to another audio blog by HAILO. Bringing you innovations and insights into AI on the edge. Now let's get started.
Host 2:Video management systems, also known as VMS, collect inputs from multiple cameras and other sensors addressing all related aspects of video handling, such as storage, retrieval, analysis, and display. Video management systems are typically used in the security and surveillance space, enhancing personal safety in public areas, office buildings, transportation terminals, medical institutes, and more. Other typical uses include the extraction of business intelligence through user behavior analysis for the purpose of customer experience improvement in retail and other industries. Traditionally, the analysis of multiple video streams used to be laborious, relying on human perception for visual identification of events happening across a multitude of video feeds. This method has many disadvantages as it is difficult to scale, violates people's privacy, and is prone to errors due to operators fatigue, leading to false alarms, missed occurrences, and inefficient use of resources.
Host 2:Nowadays, deep learning is enabling the automation of video analytics tasks, thereby allowing for scalability and improvement in overall performance. This eventually leads to lower total cost of ownership. According to a recent market research, the video management system market size is expected to reach $31,000,000,000 by 2027, growing at a compound annual growth rate of 23.1% between 2022 to 2027. The key drivers for this growth are increasing security concerns and rapid adoption of IP cameras for surveillance, security, and retail applications. There are multiple possible configurations to a VMS system, depending on the number of video channels, the required video analytics applications, and the system cost.
Host 2:However, a typical video management system will include, in addition to cameras, a network video recorder or VMS server, a display like a mobile phone, single screen or multiple screens, or others, and a storage space which could be local or cloud based. To enable real time and accurate video analytics, video management systems require high performance computing. Artificial intelligence is transformational to this market as it allows faster and more accurate event identification at a lower cost. For this reason, AI powered video analytics are being rapidly adopted by VMS software providers. AI video analytics could be introduced to any and all of the system components.
Host 2:For example, at the data source on smart cameras, at the aggregation point such as smart gateways or network video recorders, or in the cloud, which is sometimes administered in the form of video surveillance as a service. For a more detailed review of the different VMS configurations, and how to design AI into each of them, refer to HAILO's white paper named, when surveillance meets intelligence, available on the HAILO website. Let's talk about some of the key benefits that AI brings to the table. 1st up, enhanced safety. Spotting relevant events, hazards, and regions of interest in each video stream, triggering a response to predefined events, creating video metadata to enable history search, and enabling anonymization for improved privacy.
Host 2:Next, improved network utilization. Streaming relevant events only reduces bandwidth which results in further cost saving. And lastly, optimized storage utilization, removing irrelevant or uninteresting content to minimize storage space and enhance cost efficiency. The use of AI powered video analytics in VMS is threefold. Event detection, response triggering, and data analysis.
Host 2:Let's describe each one of them starting with detection. The first layer of video analytics is scene understanding and meta data extraction, for the purpose of both real time response, as well as long term storage for future search and analysis. Multiple neural networks can be deployed for this phase, leveraging advanced algorithms to perform deep learning tasks. These tasks serve as the foundation on which more complex tasks identify predefined events and trigger specific response. Let's review the key tasks that support detection.
Host 2:Object recognition. This task involves identifying specific objects or a class of objects within a frame and could be used to distinguish between classes of objects. Counting. Here, the goal is to count the number of objects in a specific area, especially in places where occupancy is limited and has safety implications. Density estimation.
Host 2:Unlike counting, this focuses on understanding the overall density in a scene. It's especially relevant for situations in which tracking the precise number isn't critical, but recognizing how crowded an area is can be informative for decision makers. Object. Attributes. This task supports identifying a person or an object in a specific scene or re identification of the same unique entity in the same scene over time or across multiple scenes.
Host 2:Gesture estimation. This is the task of analyzing a sequence of gestures, which enables interpretation of specific behaviors for the purpose of behavioral analysis. Distance measurement. This task helps calculating the distance between 2 or more people or objects based on the accurate three d location of each entity in a defined space. The second layer of video analytics is response and specifically the real time event triggering based on insights from the detection phase.
Host 2:In this phase, predefined events trigger specific responses, such as setting off an alarm or alerting operators, security personnel, or first responders, or triggering endpoint actuators. For example, to grant access for authorized personnel. Here are some use cases in which event triggering based on AI video analytics can be impactful. Crowd management. Through people and vehicle counting and density estimation, the accumulation of large number of entities can create an event in a specific location, which may trigger a response or call to action such as load balancing and dynamic traffic management.
Host 2:Perimeter protection. Face and person attributes as well as gesture estimation and distance measurement, can all serve to secure and protect an area with restricted access or an unsafe zone. Social distancing. Detecting whether people maintain social distancing in public places, supports health authorities in their effort to contain a pandemic. Behavioral analysis.
Host 2:Recognizing distress signals may trigger an alert to call first responders to the scene as soon as possible, and much faster than a call to 911 would. Lost person or unattended object. Helpful for security personnel searching for lost people or luggage, or tracking down suspects based on property indexing and gesture estimation. License plate recognition. Can be used for access control and billing in parking lots or garages.
Host 2:The 3rd and last layer in video analytics is analysis, including indexing, storage, and retrieval of metadata. Artificial intelligence is leveraged to cost efficiently index and store relevant information from video streams. Deep actionable insights can be extracted by exploring patterns over long term observations and joint analysis of multiple points of view. Let's break it down. Indexing and recording is used to efficiently manage the data and reduce communication and storage costs.
Host 2:Artificial intelligence is used to differentiate between meaningful events and background footage. This enables the system to record and store only significant events as defined by the user. Summarization is used to create an edited and concise digest of the camera input, which only includes the significant events and insights, cropping out irrelevant footage. Data extraction is used to identify patterns in stored data, based on the metadata extracted from the video stream. The better and more elaborate this metadata is, the better the results and insights will be.
Host 2:The quality of the metadata relies on the quality of the analytics, and this is where advanced algorithms exhibited by deep learning come into play. How does hailo fit into the story? Hailo offers powerful and scalable AI processors that integrate seamlessly with video management systems through a robust ecosystem of VMS players. Hailo enables advanced analytics on the edge for improved performance at a lower total cost of ownership. Hailo's AI Processors offer a wide range of form factors ranging from 13 or 26 tera operations per second with m point 2 modules for small medium VMS with up to 32 video channels, and up to 208 tera operations per second with the century PCIe cards for 1 u or 2 u based VMS systems with up to 200 video channels.
Host 2:The HAILO solutions are cost effective, offering an unrivaled AI capacity per price unit. Due to the high density and low power consumption, HAILO enables smaller form factor for any given number of channels and lower total cost of ownership, resulting in up to 75% cost saving compared to leading alternatives. Hailo enables simple and seamless integration to VMS both on the hardware and software sides. On the software side, the hailo RT driver and library send and receive data from the hailo 8 devices. They also control the devices.
Host 2:A typical flow between the hailo products and the VMS system is as follows. 1. The VMS system receives encoded video streams from the cameras over Ethernet. 2. The VMS application decodes the video streams and sends the decoded frames to 1 or more analytics plugins.
Host 2:An alternative flow, is that the decoding takes place inside the analytics plugin. 3. The analytics plugins use hailo r t to run an AI pipeline, accelerated by the hailo 8 devices. The inference results are analyzed by the plugins into analytics insights. 4.
Host 2:The analytics insights and events are sent back from the plugins to the VMS application. 5. The analytics events are shown to the users and kept in the storage for future lookup. Finally, HAILO prides itself on its robust ecosystem, which includes multiple independent software
Host 2:vendors or ISVs, who integrate their advanced video analytics with the HAILO software suite and provide plugins to leading video management systems software solutions. Our ecosystem also includes multiple OEM and ODM partners who offer multi chip hardware solutions for VMS.
Host 2:To select the best hardware platform for your needs, refer to our partners ecosystem.
Host 1:Thank you for listening to the hailo audio blog. If you enjoyed this episode, don't forget to sign up and check out more information at hailo.ai. Keep the conversation going by sharing this with your peers and never stop exploring the future of AI.