top of page

An Overview of Video Analytics in Security!


CCTV and security cameras are widely used for surveillance, but the sheer amount of footage generated makes it difficult to analyze.  Data analytics offered by Alphasoft technologies

What is Video Analytics?


Video analytics involves using computer vision and AI algorithms to extract meaning from video data. It can be used to detect, classify, monitor, track, and analyze objects in a video stream or file. Computer vision is a field of AI that deals with the interpretation of digital images. Video analytics has a variety of applications, some of which includes surveillance and security, traffic monitoring, retail customer analytics, production line inspection, autonomous driving, UAV and so much more. Most video analytics systems make use of several computer vision and machine learning algorithms and models to improve their overall accuracy and performance. A good video analytics system learns from data, reduces errors or biases, and improves its accuracy and performance over time.


Brief History


Video analytics is a technology that has been growing in recent years, offering new opportunities for security teams. However, its success has not been overnight. The first generation of video analytics struggled with false alarms because they were only able to detect pixel change or simple motion detection.


However, newer generations of video analytics are equipped with sophisticated algorithms that enable the camera to interpret what it is seeing. This, combined with advancements in CMOS sensors, has helped to improve accuracy and reduce false alarms. Today, video analytics is mostly used for forensic purposes, helping security teams and individuals to search for footage of specific events after they happen or detect specific events or objects or person(s) in real-time.



Common Applications


The application of computer vision and AI in real-time video analytics is an area of great interest and potential. Video analytics can be used for a variety of purposes, ranging from security and surveillance to marketing and customer service.

By analyzing videos in real time, organizations can gain invaluable insights into the behaviour of their customers or employees. This can help them to improve their products or services, get instant feedback or reactions, or to better understand and address issues within their organization. There are many different ways in which computer vision and AI can be applied to video analytics. For example, facial recognition can be used to identify individuals in a crowd or to track the movement of people through a space. Object recognition can be used to identify and track objects in a video feed. Also, pattern recognition can be used to detect and analyze repetitive behaviours or trends. The benefits of using computer vision and AI in real-time video analytics are numerous. By understanding the behaviour of people, organizations can make better decisions, improve their operations, and ultimately provide a better experience and security for their customers or employees.



The Role of Deep Learning Algorithms in Video Content Analytics.


Deep Learning algorithms are playing an increasingly important role in video content analytics. Thanks to advancements in deep learning research and the increased availability of video data, video analytics has transitioned from traditional algorithms based purely on computer vision to incorporating powerful deep learning techniques. Deep Learning enables technologies to detect and identify objects based on increased exposure to information. When it comes to video content analysis, Deep Learning techniques are used to teach the system how to identify and classify people and objects in videos. By doing so, a video analytics solution can enable users to accelerate investigations by searching and filtering video based on specific criteria; attain situational awareness reports based on video objects and behaviours, and derive operational intelligence by visualizing the video data into dashboards and heatmaps for evaluating trends.

Deep Learning is becoming an integral part of analytic development, and we are continually discovering its powerful applications as research and development advance further. Today, it’s helping us solve problems more quickly and more accurately than ever before and even address new challenges that we never expected to overcome!


CCTV and security cameras are widely used for surveillance, but the sheer amount of footage generated makes it difficult to analyze. Most footage is never utilized, even in post-incident law enforcement or security investigations where it could serve as critical evidence. With the help of deep learning, we can now easily outsource the gruesome task of detection, tracking, monitoring, analytics, and investigation of video footage to machines.



Benefits of Real-Time and Proactive Alerting and Response.


Video content analytics is an emerging technology that is quickly becoming essential for organizations that want to maximize the value of their video surveillance camera networks. Real-time alerts and notifications can help security staff detect unusual or suspicious behaviour, which can then be responded to immediately in order to improve public safety, security, and customer experience.


Video analytics can be used for security, traffic monitoring, crowd control, sports analysis, social distancing, face mask detection, and so on. Video analytics systems can be integrated into other physical security and building management platforms to provide more intelligent data for security and facilities managers. Video analytics can be used to prevent incidents, save on costs related to damage of properties and assets, disruption to business continuity, and manage staffing attendance. They can also be connected to other systems, such as lighting controls or access management, activating them only when there is a need.


Computer vision is a field of AI that deals with the interpretation of digital images. Video analytics has a variety of applications, some of which includes surveillance and security, traffic monitoring, retail customer analytics, production line inspection, autonomous driving, UAV and so much more. Data analytics offered by Alphasoft technologies

How Video Analytics is Applied in Different Industries.


Video analytics is a versatile tool that can be used or employed in a number of different settings, from retail to healthcare to smart cities.


Retail

Video analytics can be used to track customer behaviour and experience, in order to improve selling strategies. It can also be used to improve security and prevent shoplifting and other crimes.


Healthcare

In healthcare, for example, they can be used for security purposes, to monitor patient care, and to check for falls or missed medication.

Smart cities.

For example, they can be used to control traffic light systems, crowd control, monitor traffic, and detect crimes in smart cities. They can also be used to detect hazards such as vehicles stopping in unauthorized spaces, erratic driving, and vehicles involved in an accident.


Logistics and Manufacturing

In logistics and manufacturing, video analytics can streamline inventory management and production, detect defects or anomalies, improve quality control and efficiency, and assist in security processes. They can also help to early detect incidents and accidents.




Comments


bottom of page