In recent years, the integration of artificial intelligence (AI) into various industries has been nothing short of revolutionary. One area where AI has made significant strides is in video surveillance.
Traditionally, video surveillance systems relied heavily on human monitoring, which was not only resource-intensive but also prone to errors. The advent of AI has changed the landscape of video surveillance, enhancing its capabilities in ways that were once unimaginable.
In business operations, video surveillance has swiftly become indispensable. Its capability to enable personnel and planners to oversee unfolding events in real-time has been made possible by modern technology, thereby reducing response times.
Enter artificial intelligence (AI): you may be contemplating the roles of AI in video surveillance and how it can enhance existing capabilities. Look no further, as this article delves into the significance of AI in video surveillance, exploring its benefits, impact, and evolution over the years.
What Is the Current State of Video Surveillance Evolution?
In the past, video surveillance systems faced physical limitations, often requiring on-site setup and monitoring. The advent of Video Surveillance as a Service (VSaaS) has revolutionised this landscape. VSaaS, a cloud-based system, allows security operators to remotely monitor multiple locations. Cameras transmit video to central cloud storage via the Internet, eliminating the need for on-site management.
AI in Video Surveillance
What exactly is AI in video surveillance? AI’s application in video surveillance revolves around specific software programs that can be installed on devices. These programs are then utilised to analyse images, audio, and video content to recognise and identify individuals, vehicles, objects, and behaviours with specific configurations.
At the heart of AI in video surveillance lies computer vision. This refers to an algorithmic process in which a given object is compared to thousands of reference images, following a flowchart-like procedure. AI is tasked with determining whether the given object shares common characteristics with the reference images, such as height, colour, shape, and more.
A key advantage of artificial intelligence lies in its capacity for self-learning. Once trained, AI can conduct analyses swiftly, with heightened efficiency, and increased accuracy.
A recent breakthrough in the realm of AI applied to video analysis involves the integration of behavioural analysis. This represents a type of non-rule-based AI that is entirely self-educated. In essence, no initial programming input is needed from the user. The AI autonomously learns to recognise behavioural patterns in people, objects, vehicles, and events by leveraging its own observations. It assesses characteristics such as speed, colour and more, refining its understanding over time.
Another notable advancement is the integration of AI into edge devices, a capability demonstrated by Quantum. This facilitates accelerated collection, processing and analysis of video streams, contributing to more prompt and efficient results.
Applications of AI in Video Surveillance
AI has become integral to video surveillance, significantly elevating standards in security and public safety. Specifically designed AI algorithms enable real-time monitoring of video streams, enhancing accuracy and reliability. The capacity for real-time analysis empowers personnel to promptly respond to safety-critical situations.
An additional benefit is the use of AI for anomaly detection. By training AI systems to recognise specific situations or triggers as anomalies, such as abnormal behaviour, the technology aids in risk reduction. Automated systems can then detect suspicious events like unauthorised access or unattended luggage.
AI also contributes to safety norms by optimising crowd management. Variables such as crowd density, dwell time, speed of detected objects, and trajectory maps are leveraged to regulate the flow of people effectively. This ensures timely assistance in case of emergencies, preventing issues like overcrowding, stampedes, theft, harassment, panic or health-related incidents.
Moreover, AI enhances the reliability of motion alarms by mitigating potential false alarms and ensuring accurate data analysis. This comprehensive approach strengthens the overall effectiveness of video surveillance systems.
Other applications include:
- Real-time Traffic Analysis: Harnessing AI methodologies, this segment involves the examination of historical and current traffic data to comprehend patterns and trends in traffic flow. Predictive analysis aids traffic planners in anticipating future conditions. This, in turn, enables effective resource allocation, optimised routing to minimise congestion and adjustments to traffic signal timings.
- Incident Detection and Management: AI-powered systems play a crucial role in identifying traffic incidents including accidents, wrong-way driving, overspeeding, and road blockages. Upon detection, this information facilitates prompt dispatch of personnel to the site, ensuring a swift response. Additionally, it expedites supplementary actions such as rerouting traffic away from affected areas.
- Adaptive Traffic Signal Control: Traditional traffic signal systems operate on fixed schedules, leading to inefficiencies in unforeseen circumstances and suboptimal traffic flow. Adaptive traffic signals dynamically align with shifts in demand, recognizing peak conditions and adjusting timings accordingly. This adaptive approach optimises traffic flow, giving priority to high-traffic roads and reducing overall congestion.
- Security Functions Analogous to Traffic Management: Similar to AI’s role in enhancing security functions like anomaly detection and motion alarms, it also finds application in traffic-related functions such as license plate recognition. Evolving from manual detection, license plate recognition has become increasingly automated, contributing to the overall effectiveness of traffic management systems.
Urban Planning & Smart Cities
At the heart of the concept of smart cities lies a reliance on data-driven decision-making. A city comprises multiple interconnected systems such as road traffic, rail and water-based transportation, infrastructure, and parking facilities. To effectively coordinate these diverse elements, access to consistent, reliable data and the ability to perform rapid analysis are essential.
Utilising AI and machine learning, large datasets can be analysed to predict future events. The advantages of a smart city are manifold, including sustainable and intelligent transportation networks, increased walkable spaces, enhanced infrastructure, reduced vehicular emissions, and more efficient resource consumption.
The fundamental goal of smart urban planning is to establish equity among all residents of the city, empowering governmental bodies to make decisions that are environmentally friendly, sustainable, and forward-looking.
AI-Driven Approaches in Video Surveillance
Video Analytics and Real-Time Monitoring
Utilising AI-based systems facilitates the real-time monitoring of video data and empowers analytics functions such as detection, path and position mapping, speed and dwell time measurement, and trajectory mapping. These functionalities enhance the comprehension of video data, allowing personnel to proactively address potential security risks, safety-critical incidents, and overall management.
Furthermore, the advantage lies in the efficient and reliable collection and summarisation of large volumes of data to generate relevant insights. Heat maps, for instance, enable users to visualise extensive datasets through colour-coded maps, simplifying the identification of key performance indicators (KPIs).
Object detection, a fundamental process within video analytics systems, leverages AI to identify and categorise objects based on specific use cases. This includes vehicles (cars, trucks, vans, bicycles) and pedestrians in traffic scenarios or individuals in high-footfall areas like stadiums or shopping malls.
By employing object detection, users can access crucial figures such as people count and crowd density, gaining a comprehensive understanding of the monitored situation. This proves particularly valuable for formulating appropriate action plans; for instance, in a crowded train station, maintaining a consistent count of people aids in estimating occupancy, peak demand, resource usage, and staff deployment.
Facial recognition, another AI-oriented feature, facilitates demographic studies in video feeds by mapping details such as age and gender. This provides users with an overview of relevant customer groups for various purposes.
AI enhances video surveillance by enabling automated alerts based on specific or predefined criteria in video streams. For example, it can automatically detect instances such as jaywalking or a vehicle running a red light, promptly notifying the user. This reinforces security measures, making processes safer and more accessible for all participants.
Minimise Downtime, Cut Costs, and Simplify Operations
The integrity of video surveillance infrastructure is paramount for safeguarding individuals, locations, and business activities. The Unified Surveillance Platform (USP) software surpasses traditional NVRs by amalgamating the computing and storage capabilities of multiple servers into a resilient, high-performance, cost-efficient and adaptable software-defined system. This system effectively records and stores video and other physical security data. Crucially, by avoiding dependence on a single NVR, vital video data remains accessible even in the event of hardware failures.
When utilised alongside our comprehensive solutions for video surveillance, encompassing long-term archiving and ransomware protection, organisations can be confident that their video surveillance and other physical security data are consistently accessible. This ensures the ongoing safety and security of the individuals and places under protection.
To discuss your requirements, get in touch with our experts today.