How does AI video analysis technology change the pattern of security monitoring?

Advances in GPU-based processing technology, neural networks, and deep learning capabilities have led to next-generation AI analytics, including AI video analytics, which provide industry solutions with high efficiency and precision. How does AI video analytics differ from "traditional" video analytics? What are the advantages of this technology and how can it be applied?

Challenges of "Traditional" Video Analytics

"Traditional" video analytics, a technology that has been widely used in video surveillance for over a decade, often has more expectations than it can actually deliver. Pattern recognition and object/motion detection are possible, but there are limits to the extent to which they can prevent and resolve accidents. Accuracy is also an issue, for example license plate recognition is not 100% accurate and face recognition is notoriously difficult to perform reliably. Additionally, frequent false alarms can reduce accuracy and increase the workload of security personnel.

What is AI Video Analysis?

AI Video Analytics utilizes cutting-edge technology to digitize video footage to improve detection accuracy and classification capabilities to identify critical events and suspicious activities. Driven by artificial intelligence and deep learning, video intelligence software detects and extracts objects in videos, recognizes them based on a trained deep neural network, and then classifies each object to enable intelligent search, filtering, alerting, data aggregation and visualization features. With deep learning in place, accuracy continues to improve and false positives are reduced, resulting in increased operational efficiency and dramatically reduced investigation time.

Benefits and Enhancements of AI Video Analytics

The key advantage of AI video analytics is its ability to leverage existing video surveillance infrastructure and transform stored video data into searchable, actionable, and quantifiable intelligence to improve productivity.

Object classifications include, but are not limited to:

People - Age, Gender, Race, Clothing Color

Vehicle - size, type (e.g. car vs. truck), color, direction of travel

Animal - type (e.g. cat vs. dog), color

Inanimate objects (eg bags) - size, state, type

Al can be used to train the system to generate real-time alerts based on certain behaviors, such as:

directional movement

to wander

People Counting

Leftovers

Object removed

AI-Based Behavior Analysis

Video Surveillance Health Monitoring

Health and Safety Compliance

Summarize

AI video analysis provides highly accurate real-time alerts with a significant reduction in false positives, enabling security managers and business operators to proactively respond to changing conditions in the environment. An additional layer of analytics converts recorded video data into actionable, valuable information, facilitating rapid and precise investigations by pinpointing people and objects of interest.

By extracting and aggregating video metadata (such as male, female, child, vehicle, size, color, speed, path, etc.), users can quantitatively analyze their videos and use this data for other purposes, helping to improve the security of their surveillance infrastructure. Operational efficiency.

More and more artificial intelligence technologies have been integrated into the field of video surveillance, especially in security surveillance, such as face recognition, face detection, license plate recognition, behavior analysis technology, etc., with TSINGSEE EasyCVR video fusion Take the cloud platform as an example. It can capture, detect and identify people, vehicles and objects in the video surveillance scene, and intelligently remind and notify abnormal situations. It has been widely used in security monitoring, intelligent analysis, traffic verification and other scenarios.