A Motorola Solutions company provides trusted security solutions to the global market.
Avigilon designs, develops, and manufactures video analytics, network video management software and hardware, surveillance cameras, and access control solutions. Avigilon’s solutions have been installed at thousands of customer sites, including school campuses, transportation systems, healthcare centers, public venues, critical infrastructure, prisons, factories, casinos, airports, financial institutions, government facilities, and retailers.
LILIN was established in New Taipei City, Taiwan, in 1980. The company has quickly grown to be among the world’s leading manufacturer in the advanced IP video surveillance industry. LILIN is dedicated to design, development, manufacturing, and marketing of a broad range of networking surveillance solutions, looking forward to providing the best solution for our customers.
Hikvision advances the core technologies of audio and video encoding, video image processing, and related data storage, as well as forward-looking technologies such as cloud computing, big data, and deep learning. Over the past several years, Hikvision deepened its knowledge and experience in meeting customer needs in various vertical markets, including public security, transportation, education, healthcare, financial institutions, and energy, as well as intelligent buildings. Accordingly, the company provides professional and customized solutions to meet diverse market requirements. In addition to the video surveillance industry, Hikvision extended its business to smart home tech, industrial automation, and automotive electronics industries — all based on video intelligence technology — to explore channels for sustaining long-term development.
Dahua Technology is a leading solution provider in the global video surveillance industry. Dahua’s core video surveillance product line, including network cameras, NVRs, and HD over coax, can be applied in many sectors, including banking & finance, government, industrial, retail, sports & leisure, transportation, and energy.
Traditional face recognition algorithms are based on local features. The algorithm first detected a series of relatively invariant points in the face (fiducially points: corners of the eyes and mouth, nostrils, etc.). Typically the number of points was between 10 and 300. Then, usually after aligning the face, the algorithm extracted information of interest from these local regions, one per point, using certain visual features (e.g. wavelets, HOG, SIFT, SURF… or variations of them). The different pieces of information were concatenated into a single feature, and optionally its dimension was reduced to make it easier to store and fast to compare to other features.
In that kind of algorithms, the number of points became synonymous with the accuracy of the prediction. Which is not entirely true: if the points are well-aligned, more points generally give more accuracy for a given method, but the accuracy is extremely dependant on the particular exactness feature and classifier on any given facial appearances.
Deep learning algorithms, which are now state-of-the-art on most computer vision applications, work different. They apply banks of convolution and non-linear filters repeatedly over an original image. Each layer of application processes the image and extracts higher-order information. After many layers of these filter banks (typically between tens and hundreds), faces are encoded directly into small templates which are very fast to compare, and yield much more accurate results.
The interesting thing about deep learning is that the way to extract visual features is not manually defined, as before, but it is optimally learned by the network itself during training. All the processes of face alignment /frontal, localization of interesting regions, etc. are done internally by the network. You do not need to tell the algorithm where the interesting points are, nor how to extract the information, as it learns by itself.
Deep learning is a branch of machine learning. It is particularly suited for certain learning tasks, as it tends to scale accuracy and generalization with the training data (thus benefiting from large amounts of it), and it automatically learns the best internal representations of data that optimize a learning goal, as opposed to some traditional learning techniques that required manual handcrafting of such representations.
Use of NVIDIA GPU
NVIDIA GPUs are ideal for training deep neural networks, speeding a process that could otherwise take a year or more to just weeks or days. That’s because GPUs perform many calculations at once-or in parallel. And once a system is “trained,” with GPUs, scientists and researchers can put that learning to work. That work involves tasks once thought impossible.
GPUs are used to train these deep neural networks using far larger training sets, in an order of magnitude less time, using far less data center infrastructure. GPUs are also being used to run these trained machine learning models to do classification and prediction in the cloud, supporting far more data volume and throughput with less power and infrastructure.
Guangzhou Balun Electronics Limited was established in 1993 by two ambitious young men who abandon their so call great job civil servant. For 20 years, they dedicated their time in the field of enterprise level due to weak electronics industry. As the solution supplier and manufacturer of Audio & Video system, they are using artificial intelligence to change the world step by step: HD Video Conference Discussion System, IP Network Public Address System, Voice Alarm Evacuation System, Bank.
Changsha SPON Communication Technology Co., Ltd. is a national high-tech enterprises which is own core intellectual property and innovative research & development capabilities. We have rich experience in IP video and audio products R&D and industry application. The products are widely used in education, public security, military communication, at present.
Ubiquiti Networks is an American technology company started in 2005. Based in New York, NY, Ubiquiti manufactures wireless data communication products for enterprise and wireless broadband providers with a primary focus on under-served and emerging markets.
Ubiquiti sells wired and wireless networking products under multiple brand names. The company also sells grid-tied solar kits.
Ubiquiti’s first product line was its “Super Range” mini-PCI radio card series, which was followed by other wireless products.
The company’s Xtreme Range (XR) cards operated on non-standard IEEE 802.11 bands, which reduced the impact of congestion in the 2.4 GHz and 5.8 GHz bands. In August 2007 a group of Italian amateur radio operators set a distance world record for point-to-point links in the 5.8 GHz spectrum. Using two XR5 cards and a pair of 35 dBi dish antennas, the Italian team was able to establish a 304 km (about 188 mi) link at data rates between 4 and 5 Mbit/s.
The company (under its “Ubiquiti Labs” brand) also manufactures a home wireless mesh network router as a consumer-level product.
ZKTeco, founded in March 1998, is a world-leading enterprise specialized in Hybrid-Biometric Verification technology. ZKTeco currently owns the largest quantity of patents and intellectual property rights in the field, and applies biometric verification technology to smart office, smart financing, smart traffic, and smart security, with a service network covering the entire world.