Video analytics uses ARTIFICIAL intelligence to accomplish various tasks by applying computer vision and deep learning to video clips or live video streams. Video analysis is sometimes called video content analysis or intelligent video analysis.

New video analysis technologies are rapidly gaining popularity. The main adopters are enterprises seeking to use the latest Al technology to solve long-standing problems, as well as enterprises that have been running video surveillance systems before the advent of artificial intelligence (Al).

Advances in deep learning and machine learning (two subsets of Al) are enabling video analytics to change the traditional landscape that once required human intervention to successfully automate tasks.

The ability to use deep learning for video content analysis, real-time video processing, and improvements in the accuracy of video recognition software are driving the video analytics market forward all the time.

AI video intelligent analysis workflow

** How does target detection in video analytics happen? ** Real-time object detection in video sources has been possible for years thanks to algorithms such as Mask R-CNN or YOLO. These algorithms are pre-programmed to detect differences between target objects. For example, they allow video analytics programs to detect and track objects such as vehicles, people, and traffic lights in real time. These targets are tagged and can be used in scenarios such as vehicle or people traffic statistics.

Motion detection and video analysis

Video motion detection is a method to define the activity in a scene by analyzing the differences in a series of images. Video motion detection is usually carried out through processes such as frame reference or pixel matching. Frame references and pixel matching involve detecting horizontal or vertical changes between video frames and treating them as detections. The technique is commonly used to analyze video using motion detection. It can be built into network video products, such as IP/CCTV cameras, or provided through video management software.

Industry-specific AI video analytics

AI Video Analytics has been working to provide security solutions by creating common methods for identifying and detecting different objects in video streams. Such technologies can be used to track people or objects of interest in videos or to identify and detect intruders. For these purposes, using video analytics allows certain objects to be flagged and alerted to suspicious behavior.

Vertical motion detection

A specific example of video analysis for security might be a fence climb detection system. Intelligent systems are often trained to know that it is normal for people to step out of a fence, but that climbing or struggling on it is abnormal behavior. Video analysis software is trained to recognize subtle differences in motion.

The AI system can receive a live video feed from the smart camera and detect regular and irregular behavior of the electronic fence in real time. If someone starts climbing the fence, the software recognizes vertical movement as an unusual event and raises an alarm. By contrast, if someone walks next to a fence, they will produce horizontal movements that the detection system will not classify as suspicious activity.

There are many different variations of video analytics applications on the market. For example, some video analysis can be used to detect people climbing fences within visual range. In this application, the video analysis function is based on an integrated object detection algorithm running directly on the device, rather than an external server, to perform detection in real time (edge computing).

Multiple AI algorithms can run simultaneously, and alerts can be sent to managers via messaging, phone or video management systems.

Video source object classification

Video source object classification involves detecting dangerous objects in real-time camera sources or in a given video. Sometimes even small differences between objects that are difficult for security personnel to see in front of a camera can be detected by video analysis programs, which are trained to spot tiny differences that could pose a potential security risk.

For example, X-ray security could use trained video analytics to categorize objects in real-time feeds of luggage during security checks to identify specific objects of interest, such as sharp tools. Such techniques are already being implemented around the world as the accuracy of AI algorithms improves.

Tracking behavior

Similar to the motion detection discussed in the electronic fence example, other types of behavior are relevant grounds for video analysis to be able to classify. For example, behavioral tracking involves human behavior related to itself and larger objects (such as vehicles), and the scenarios it involves are generally used for regional security.

  • ** Wandering detection: ** Video analytics is trained to issue an alarm notification when a person or vehicle stays in a defined area longer than the user-defined time limit allows. For the safety of the area, an alarm can be activated depending on the situation. This behavior is very effective in real-time reporting suspicious behavior in pharmacies, ATMs, and factory complexes.

  • ** Parked vehicle detection: ** This part of the video analysis technology helps prevent vehicles from sitting idle for long periods of time or parked in unauthorized locations. Detect vehicles that spend more time in the vicinity of a sensitive area than the user-defined time allows. This behavior is ideal for preventing vehicles from blocking loading and receiving docks, enforcing parking rules, and reducing waiting time for vehicles at valet services or parking lots. A vehicle stopped on a moving road can also indicate an unreported accident or vehicle problem, and the technology can alert traffic authorities to timely action.

  • ** Camera damage: ** Advanced video loss detection identifies when a live video stream is compromised or tampered with. For example, if a vandal paints or overlays a lens or reaches out to move a fixed camera away from the intended scene, an alarm will be triggered.

AI video intelligence analysis market

Traditional players in the AI video intelligent analytics market include Cisco, Avigilon, AxisCommunications, Aventura Systems, Genetec, IBM, IntelliVision, Bosch Security, huawei, etc. The video analytics market is divided into services and software. Most companies focus on creating video analytics products (services) that can be consumed or software (software) that the products need to succeed.

The most common scenario applications in the video analytics market involve security: event detection, intrusion management, population statistics, traffic monitoring, automatic license plate Recognition (ANPR), facial recognition, AR, attitude estimation, and so on. In addition, AI video intelligent analysis technology is also playing an important role in retail, medical and hotel scenarios.

Recently, new computer vision platforms have been introduced to the market, allowing companies to offer customized video analytics applications. Video analytics solutions built using low-code development platforms help enterprises adopt custom video analytics solutions while providing the power, speed, simplicity, and flexibility of off-the-shelf software solutions.

The technology in TSINGSEE Video is worth mentioning because it will guide the development of the entire video analytics software field. Combined with years of technical experience in the video field, the deployed Al algorithm is used to process a large number of smart camera video sources in real time to achieve massive video access, intelligent analysis and processing capabilities. At present, EasyCVR has achieved face detection, traffic statistics, vehicle detection, license plate recognition and other AI intelligent recognition technology research and development, and is widely used in traffic, logistics, security, fire and other scenarios. In the future, TSINGSEE Video will provide more industry solutions based on deep learning video analysis.