Intelligent Video Analytics: The Future of Surveillance

Intelligent Video Analytics: The Future of Surveillance

What is Intelligent Video Analytic?

In the world of video surveillance and security, video analytics and artificial intelligence (AI) are all the rage. Massive amounts of video data are generated by video surveillance systems. Most of the video is never watched or reviewed, and it is impossible for a human to effectively watch so many cameras. As a result, security incidents are missed, and suspicious behaviour is not detected in time to prevent incidents from occurring. These challenges, as well as innovative technology, have played a significant role in the development of video analytics.

What is Intelligent Video Analytic

Algorithms and machine learning are used in video analytics and artificial intelligence (AI) for video surveillance to track, organize, and examine massive amounts of real-time footage. Video intelligence software analyzes audio, images, and video from IP cameras to recognize humans, vehicles, objects, attributes, and events using AI and deep learning. Video analytics are software applications that generate automatic descriptions of what is happening in the video (metadata) that can be used to list people, cars, and other objects detected in the video stream. This data can then be used to make decisions, such as whether security personnel should be notified or whether a recording should begin. You can effectively manage tens of thousands of CCTV and IP cameras with the support of intelligent video analytics software.

Security View Camera
Control Room Monitoring Feeds

Types of Intelligent Video Analytics

Several forms of AI and video analytics are used for video surveillance, including:

  • Detecting line crossings and intrusions
  • Traffic monitoring
  • Motion detection
  • Counting subjects (people or things)
  • Object and person detection
  • Automatic number plate recognition, license plate recognition
  • Facial Recognition and Analysis

Advantages of Intelligent Video Analytics include:

Cost savings - With true cloud technology and micro-services architecture, less video is sent over the network, reducing network load and storage requirements.

Time savings - monitoring and searching for recorded video is simpler and faster, allowing operators to manage more cameras.

Increased efficiency - Automatic video surveillance for security incidents reduces false alarms by a substantial margin and aids in crime prevention.

Business value creation - By integrating video data into in-house and third-party systems, such as POS data and headcounts from retail store entrance cameras, the surveillance system can contribute insights and new solutions to other business functions.

A typical camera security system was designed to capture and keep an eye on specific locations. The technology was created so that after an incident, a person may go back in time and rewind to the time of the incident to locate answers or the offenders. But in today's dynamic environment, prevention outweighs cure. Real-time event updates quickly became essential for ensuring the safety of onlookers. Imagine a central command room with monitors and analysts focusing on live feeds to scrutinize even the smallest movements that might determine the difference between life and death.

With an ever-increasing population, public spaces are becoming larger; some megastructures may have 1000+ cameras. There will always be a blind spot for unchecked feeds with the control room rotating 1000+ camera views and about a dozen pairs of eyes looking for potential danger. Some theme parks have vehicles equipped with cameras that capture live feeds wherever they go, which only complicates an already difficult job.

As technology advances, there are already deep learning AI-capable computers that can replace hundreds of security guards' eyes by continuously monitoring live video feeds for suspicious activity even when the feed is not visible on a monitor (near-zero blind spots). Any packages left behind are marked and displayed right away on the monitors by the computers, which are programmed to recognize a person's joints and limbs for certain actions. The advantage of such systems is that they almost eliminate blind spots because every frame of every video is examined by the computer.


Edge-AI technology seems like the ideal way to evaluate large amounts of video streams, but there are obstacles. Similar difficulties to those encountered when installing a roadside unit will arise when implementing a real-time edge AI surveillance system in a big open area. Consider the following:

Challenges Edge-AI technology

Power Delivery

The majority of roadside power delivery are high-powered AC mains, while some deliver low voltage 18V in the circuit's boost segment. The power option is considerably more constrained if the device or camera is mounted on a vehicle.

Cabling Needs

Data and power cables may be needed to connect to several cameras.

Camera Support

The supported camera type must be able to withstand adverse weather conditions and changing lighting while still providing detection and recognition capabilities.

Edge-AI Industrial PC

For edge processing, the deployed industrial PC needs to be AI-capable and strong enough to simultaneously detect numerous sources.

Environmental Factors

Depending on where it is mounted, exposure to direct sunlight or wind may cause overheating or vibration. While the system must overcome obstacles like shock and vibration when the vehicle is moving.

Means of data communication

Several deployment locations would be needed for the monitoring system, and for real-time alerts, the industrial PC must be capable of instantly transfer and upload the data.


A system for detecting and identifying human limbs and joints was first created by our customer. The system can follow a person's movement, such as a kneeling motion, by identifying the limbs. Perhaps you could train the industrial PC to detect a certain gesture or movement using deep learning. For instance, striking someone with a right or left hook.

Solution Industrial PC

Our customer chose the Neousys NRU series edge industrial PC to have a system mounted or that can operate in-vehicle conditions, and have cameras operate in various lighting conditions while in the rain. It supports GMSL cameras with IP67 waterproof characteristics, high dynamic range (>120dB HDR), auto white balance (AWB), and LED flickering mitigation (LFM), and is unaffected by lighting conditions, whether sunny, overcast, or dark. The NRU series edge industrial PC is powered by NVIDIA® Jetson AGX Xavier, which provides TFLOPS inference performance, making it ideal for image recognition applications.

The NRU series systems have also been tested to be MIL-STD-810G compliant to withstand the shock and vibration conditions of a moving vehicle; are compact enough to fit into confined space installations; include ignition power control to protect the industrial PC from power surges at startup; and offer true wide temperature operation capabilities.

NRU-110V Series

NVIDIA® Jetson AGX Xavier™ Edge AI Fanless Industrial PC Supporting GMSL Automotive Cameras

NRU-120S Series

NVIDIA® Jetson AGX Xavier™ AI NVR Fanless Industrial PC for Intelligent Video Analytics

NRU-52S+ / NRU-52S

Rugged NVIDIA® Jetson Orin™ NX/ Xavier™ NX Edge AI Computer with 4x PoE++ Ports for Intelligent Video Analytics

NRU-220S Series

NVIDIA® Jetson AGX Orin™ AI NVR for Intelligent Video Analytics

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