In 1950, the fishing village of Shenzhen in south-east China had 3,148 inhabitants. By 2025 it is predicted that number will exceed 12 million. In 1900 just 14 percent of people on earth lived in cities1 but today over 55% of the world’s population lives in urban areas, and the rate continues to grow. The UN predicts that by 2050 the percentage of people living in urban areas will edge closer to 70%2.
Improving cities is a pressing global need as the world’s population grows and our species becomes rapidly more urbanized. Thanks to the relative ease with which local governments can now gather real time data, combined with the capabilities of artificial intelligence, cities can now realize new ways to run more efficiently and effectively.
Almost all major cities now have a network of CCTV cameras installed, both in and around public installations (such as airports, train stations) as well as on the roadside to monitor traffic. NVIDIA predicts that by 2020 there will be 1 billion cameras deployed on government property, infrastructure, and on commercial buildings3.
Not only is the number of CCTV cameras increasing, but also the quality and resolution. IHS MARKIT predicts that by 2022, up to 60% of CCTV cameras shipped worldwide will have a resolution of 4 megapixels or higher, resulting in a marked increase in the quality of useable video4. This huge source of raw video has a massive potential to be used more effectively to provide intelligence to local governments and business in order to improve public & customer safety, security and convenience.
However, the huge increase in raw video means that it will become impossible for most footage to be viewed live or after the fact by human operators. For example, manually reviewing 1 hour of video can take up to 2 – 2.5 hours4. Most video is then either deleted permanently or archived without being used for any meaningful analysis.
Advances in artificial intelligence mean that applications can now take on image recognition capabilities that allow them to detect and identify human faces, vehicle registration plates, and other objects. An intelligent video analysis platform can be used to recognize faces, license plates and objects (and compare them with a database for matching) and track the speed and movements of people and vehicles to establish patterns.
Furthermore, by using machine learning, video analysis and image recognition can become faster and more accurate over time. The more often a computer performs this sort of analysis, the more capable it becomes of correctly identifying and tagging other images in the future. Larger datasets lead to more accurate results, and feedback loops help eliminate errors.
Gorilla’s Intelligent Video Analytics Recorder (IVAR) solution extracts surveillance video insights and delivers actionable facial, vehicle and object identification results for government and commercial entities. Compatible with industry protocols and standards like ONVIF, RTSP and H.264, this solution can be deployed on edge devices connected to video cameras, or integrated directly into devices with built-in cameras. Gorilla’s IVAR offers value-added applications in different verticals like public safety, industry, retail, banking and education and provides advanced dataset services for cloud servers.
Gorilla’s IVAR also utilizes Intel’s OpenVINO™ (Open Visual Inference & Neural Network Optimization) toolkit. Based on convolutional neural networks (CNN), the toolkit extends workloads across Intel® hardware and maximizes performance. OpenVINO™ allows Gorilla’s IVAR to increase performance by 50%, enabling edge devices to conduct 1½ times more video feeds in real-time analytics. Gorilla’s clients will dramatically benefit from lower deployment costs as more video channels can analyzed on the same hardware.
Gorilla’s provides a suite of powerful backend applications which can be run on a public, private or hybrid cloud. Video streams are collected on Gorilla IVAR edge devices or edge servers for preprocessing and then forwarded for analysis. Here, unstructured video and image data is transformed into structured data via deep learning. Events are stored in software defined storage and correlated and categorized for use in biometric authentication, account management, device management, and business intelligence.
ABOVE: SCREENSHOT FROM GORILLA’S DATASET & TRAINING SERVICE
Gorilla has qualified and recommends the following GIGABYTE servers for an Intelligent Video Analytics Platform turnkey solution:
GIGABYTE’s G190-144 is an ideal compact form factor GPU server, providing a capacity of 4 x GPU cards in a 1U form factor, ideal for inferencing purposes of multiple video streams.
The R181-340 rack server combined with the D120-C21 storage server is an ideal cost and performance efficient solution for hosting the back-end applications of an Intelligent Video Analytics Solution.
The G291-280 combines GIGABYTE’s expertise in thermal and mechanical engineering to provide an industry leading density of 8 x GPU cards in a 2U form factor server. The G291-280 combines GPU capabilities with dual Intel Xeon Scalable processors up to a maximum TDP of 205W, and support for both Intel Optane DC Persistent Memory (with up to 512GB per module) and Intel Omni-Path technologies, making it an efficient and powerful platform for machine learning applications.
An international airport in Asia handles 42.3 million passengers and 2.1 billion kilograms of freight annually, making it one of the top ten busiest airports by international passenger traffic. Due to the pressing requirements to prevent terror attacks and control access of people and vehicles to restricted zones within the airport, Gorilla’s Intelligent Video Analytics Platform was deployed to provide facial and vehicle recognition from CCTV video streams.
The solution enables the following functionality:
A national police agency in Asia is responsible for the maintenance of public order and enforcement of the country’s laws, and currently has over 230,000 employees. They required a system to allow them to use citywide and countrywide video surveillance installations to intelligently and automatically monitor people and vehicle activity and to locate and track suspicious targets.
Gorilla’s Intelligent Video Analytics Platform was deployed to provide better management of local security and public safety, and enable fast action in volatile situations.
The solution enables the following functionality:
1 The Guardian Cities in Numbers: How Patterns of Urban Growth Change the World, November 23rd 2015
2 United Nations 68% of the World Population Projected to Live in Urban Areas by 2050, 16 May 2018
4 IHS MARKIT AI in Video Analytics: Improving Safety, Security, and Operations, Report by Oliver Philippou 2018