Modern fisheries have embarked on a new era of data and analytics-based technology to provide monitoring capability in a complex operational environment.
To ensure reliable monitoring and recording, the industry is turning to a reliance on Artificial Intelligence-based vision and Big Data analytics enabled by IoT technologies. By using electronic monitoring, video surveillance, sensors, and control systems, today’s fishing industry can achieve cost and risk reduction using intelligent systems.
Data centers are and will continue to serve as the backbones of our modern world. From watching your favorite shows on video streaming platforms to communicating with colleagues overseas, data centers enable the online services and resources that empower the way we live and work. As a result, ensuring the optimal operation and security of our data centers is crucial. To realize this, data center administrators require the ability to collect, consolidate, and analyze data across the board. This is best achieved via Data Center Infrastructure Management (DCIM) that leverages surveillance technologies, network appliances, sensors, and monitoring software. Through effective DCIM, administrators have an indispensable tool to measure, manage, and control data center utilization and energy consumption of all IT-related equipment and facility infrastructure components, including air conditioning systems.
In order to build the largest edge cloud network in rural environments, network-as-a-service providers utilizes edge computing and wireless technology to bring the capabilities and advantages of the cloud with IoT and AI to the remote and hard-to-reach rural areas.
To function properly, drive-thru restaurants need outdoor screens that display changing menus, promotions, offers, etc. They need a solution that ensures reliable and high-performance external displays, which can be managed remotely.
The COVID-19 virus outbreak is changing the way we commute. Train terminals, airports, metro stations are now the virus hot zone. To limit the spread of the virus and protect passenger safety, it is required to implement the new safety measures that would maintain efficient passenger traffic flow, while automatically measuring the temperature of every passenger at the checkpoint.
AI-based Edge Computing technology is finding its way into various application scenarios, and one of which is intelligent traffic management. AI-optimized video analytics algorithms enable traffic flow analysis, vehicle counting, license plate recognition and driver/pedestrian behavior prediction, generating tremendous but useful volume of real-time data, at the edge, that can be relayed to the traffic control center for proactive response with almost no latency, not only preventing accidents but also saving lives.
Intelligent surveillance solution is one of the most widely adopted AI applications as it can be deployed for a broad range of use scenarios, delivering advanced video analysis with deep learning for almost all vertical sectors. Before they can leverage the benefits such as AI-enhanced detection accuracy and flexibility, however, businesses and organizations must overcome a significant numbers of challenges before and after a deep learning model is trained because AI-powered video surveillance solutions rely on visual data capture for analyzing/solving security, safety, and operations challenges; and such reliance demands simultaneous analysis of vast volumes of video footage.