FLIR Systems, Inc. announced the FLIR Firefly® camera family, the industry’s first deep learning inference-enabled machine vision camera. The FLIR Firefly, which integrates the Intel Movidius Myriad 2 Vision Processing Unit (VPU), is designed for image analysis professionals using deep learning for more accurate decisions, and faster, easier system development. The FLIR Firefly combines a new, affordable machine vision platform with the power of deep learning to address complex and subjective problems such as recognizing faces or classifying the quality of a solar panel.
Agent Video Intelligence announced the launch of innoVi Enterprise, an innovative Deep Learning-powered video analytics software designed to meet the high-end security and safety needs of multiple vertical sectors. innoVi Enterprise includes a comprehensive suite of real-time detection rules as well as an autonomous real-time Anomaly Detection capability. Powered by Deep Learning technology, innoVi Enterprise boasts a highly accurate object classification methodology that learns to accurately classify and distinguish between various object types such as persons, cars, motorcycles, bicycles, trucks, buses and static objects.
Dahua Technology, a leading solution provider in the global video surveillance industry, officially launched its first deep-learning powered Network Video Recorder (NVR), offering advanced Artificial Intelligence features for a variety of applications. Dahua’s powerful Artificial Intelligence technology enables faster video content inquiry and rapid discovery of target objects. This opens up new possibilities for danger prevention, along with real-time alerts for the video surveillance industry.
Agent Video Intelligence announced the launch of its breakthrough Anomaly Detection capability, as part of Agent Vi’s cloud-based Software as a Service (SaaS), innoVi™. Combining Agent Vi’s extensive research into Artificial Intelligence (AI) and development of Deep Learning-powered algorithms with the company’s 15 years of experience in providing cutting-edge Video Analytics solutions, the new Anomaly Detection is a robust self-learning capability that instantly alerts users to atypical incidents.
The pace of technological innovation is such that even the most fantastic of imagined futures seem like they could easily become reality. As existing technologies reach maturity, unforeseen developments arrive ever more quickly, and innovations make the leap from consumer applications to business (and vice versa) it’s imperative that we constantly seek to find those that have the potential to add value to our own business and those of our customers.
IC Realtime announced that a time-lapse compiler and creation tool is the first feature update available for Ella, IC Realtime’s cloud-based deep-learning search engine solution that augments surveillance systems with natural language search capabilities. With this new update, users can create a time-lapse video from a range of time or by a specific query to produce more detailed and relevant results.
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.
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.
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.