IC Realtime, announces the introduction of Ella, a new cloud-based deep-learning search engine that augments surveillance systems with natural language search capabilities across recorded video footage.
Moscow continues to bring video surveillance security to the next level, through a partnership that brings artificial intelligence technology in ways that will make facial recognition more accurate and more intuitive. Moscow’s Department of Information Technologies has recently finished deploying a city-wide, 160,000-camera video surveillance system that integrates facial recognition technology from NtechLab. Yet only a portion of those is currently active, due to the cost of implementing the technology.
Seagate Technology plc announced its SkyHawk™ AI hard disk drive (HDD), the first drive created specifically for artificial intelligence (AI) enabled video surveillance solutions. Building on Seagate’s 10-year track record of delivering surveillance optimized storage performance, SkyHawk AI provides unprecedented bandwidth and processing power to manage always-on, data-intensive workloads, while simultaneously analyzing and recording footage from multiple HD cameras. Analytics on video surveillance hardware is growing exponentially, forecasted to increase from 27.6 million shipments in 2016 to 126 million shipments in 2021 (Cropley, 2017), as hardware manufacturers continue to include analytics sensors on network video recorders (NVRs).
BrainChip Holdings Ltd., (ASX:BRN), a leading developer of software and hardware accelerated solutions for advanced artificial intelligence (AI) and machine learning applications, announced that Thomas (“Tom”) Stengel has joined the Company as Vice President of Americas Business Development. Tom is responsible for driving sales of BrainChip’s spiking neural network AI technology into OEM partners and End-Users in the video surveillance, server, and storage markets in the Americas.
Avigilon Corporation (TSX: AVO), provider of trusted security solutions, showcased their new Avigilon Unusual Motion Detection (“UMD”) video analytics technology at ISC West 2017 last week. UMD is an advanced artificial intelligence (“AI”) technology that brings a new level of automation to surveillance. This technology is designed to continuously learn what typical activity in the scene looks like and focus the operator’s attention on atypical events needing further investigation.
Intel Movidius, the world leader in embedded machine vision technology, is helping bring artificial intelligence (AI) to video surveillance cameras.
Movidius participated in an announcement from Dahua Technology USA in which Dahua outlined how it is using Movidius’ Myriad 2 Vision Processing Unit (VPU) technology to power select Dahua video surveillance cameras. These cameras will go beyond traditional functions such as monitoring and recording by offering advanced video analysis features, such as crowd density monitoring, stereoscopic vision, facial recognition, people counting, behavior analysis, and detection of illegally parked vehicles.
Movidius, a developer of embedded machine vision technology, together with leading provider of video-based IoT (Internet of Things) solutions and data operation services Hikvision have announced that Movidius’ Myriad 2 Vision Processing Unit (VPU) technology will be powering a new lineup of smart cameras. Among other things, Myriad 2 will be utilized for running cutting-edge Deep Neural Networks in order to perform much higher accuracy video analytics locally.
Deep learning technologies are making it possible to process and analyze vast streams of footage. It’s an area that’s been seeing significant investment and research. Mimicking the process of the human brain, the technique uses sophisticated, multi-level, “deep” neural networks to create systems that can perform feature detection from massive amounts of unlabeled training data. Data scientists in both industry and academia are using graphics processing units (GPUs) to accelerate their deep learning algorithms. GPUs process highly parallel computing tasks —like video and graphics— quickly and efficiently.
Computer software only recently became smart enough to recognize objects in photographs. Now, Stanford researchers using machine learning have created a system that takes the next step, writing a simple story of what’s happening in any digital image. At the heart of the Stanford system are algorithms that enable the system to improve its accuracy by scanning scene after scene, looking for patterns, then using the accumulation of previously described scenes to extrapolate what is being depicted in the next unknown image.
With ‘true leadership’ spirit, what Rustom Kanga has accomplished in establishing his own business and flourishing in it is commendable. Dr. Rustom Kanga is CEO of iOmniscient, a company specializing in advanced video analytics.