Surveillance technology based on artificial intelligence (AI)
New video surveillance technology will help support security, prevent and provide advance warnings of unpredictable security risks. However, to collect important data and insights from already recorded data streams, the supervisor must be extremely focused and not miss any detail. Usually, a few security centers will monitor the entire video footage and events taking place.
With the assistance of AI technology, everything becomes much easier. AI software will aid in analyzing images and sounds captured by video and surveillance systems. AI technology can utilize image analysis algorithms to recognize faces, objects, events, and more. With AI robots, monitoring becomes a hundred times faster and ensures greater accuracy than the human eye, operating continuously.
AI technology has now advanced in various fields, including "Computer Vision," which encompasses numerous system-developed features such as:
- - Event detection
- - Scene reconstruction
- - Video tracking
- - Object recognition
- - 3D solid object scanning and modeling
- - Motion alerts
- - Image reproduction.
How AI works in image surveillance
All data transmitted from the video surveillance data stream will be provided to the AI system for analysis and processing. The system will analyze the content in real-time, identify key data patterns, and promptly send alerts to the management.
Typically, the analysis does not occur on the surveillance cameras themselves, but rather on Edge Computers technology such as NVRs (Network Video Recorders) or cloud-based services. An example of an Edge Computer system with NVR integration is the NVA-3000, specifically designed for security monitoring and machine vision systems. In the case of cloud computing, AI processing can be performed on compatible servers or through emerging services like VSaaS (Video Surveillance as a Service), which offer comprehensive security camera monitoring solutions.
Popular AI applications in surveillance systems:
Face and object recognition: AI effectively supports intelligent access control systems in sensitive areas or identifies criminals and security threats. In addition, it is possible to detect unusual and dangerous objects. This application is completely suitable for smart home applications in detecting and identifying people, objects, and pets.
Behavioral analysis: AI technology can help observe and detect unusual behaviors and events, for example, AI technology with built-in cameras for early detection of people approaching restricted areas time in works.
Motion detection: Abnormal movements within certain timeframes or in restricted areas.
Audio analysis: Surveillance camera devices often incorporate sound recording functions. The separation of frequencies and strange sounds helps smart home systems, industries or vehicle systems.
Applying surveillance technology to harsh environments
Remote monitoring of hazardous working environments, such as oil fields or coal mines (where toxic gases, fire hazards, and explosions are common) or container trains (which experience high levels of air pollution and moisture, leading to corrosion), presents significant challenges. Traditional video surveillance systems often struggle to address these issues effectively.
However, with advancements in computing and connectivity technologies, it is now possible to remotely operate and monitor such hazardous work areas. The integration of IoT cameras, on-site edge computing devices, NVR industrial surveillance systems, and broadband connectivity has greatly simplified the process.
IP cameras and related equipment are essential considerations, and devices enclosed in stainless steel, special construction materials, or equipped with built-in cooling systems can be beneficial. These measures help address various challenges, such as:
- - Detecting areas with excessively high or low temperatures.
- - Identifying regions with high radiation levels or concentrations.
- - Detecting fire sparks, explosions, or smoke.
- - Addressing humidity and environmental corrosion.
- - Monitoring continuous violent shaking caused by natural disasters like earthquakes or tsunamis.
An example of a device that can assist a security monitoring system in triggering alerts to the police, ambulance, or fire protection system during emergency situations is Lanner's LEC-2580. This device is integrated with AI technology and is also capable of connecting with drivers navigating challenging roads, experiencing vibrations or excessive movement. It provides valuable information to the driver and can transmit journey data via the internet.
How to monitor harsh environments?
The outdoor camera storage system has the capability to stream data online to portable mobile devices via a 5G connection, or data can be transmitted to a cloud provider for AI analytics. Cloud systems or edge computing devices like MEC can preprocess all the data. AI analytics technology enables on-site management and immediate notification to the security management unit in case of events such as fire, chemical leaks, floods, intrusions, and alarms.
Flying IoT and Drone Technology
Drones, also known as "Aerial IoT Devices" or UAVs (Unmanned Aerial Vehicles), have garnered significant attention in recent times. Thanks to advancements in technology such as Private LTE, 5G, and Edge Computing, drones can now be integrated into a wide range of applications.
An example of drone usage in video surveillance is seen at Skylark Labs, a technology visionary that has developed Aerial Suspicious Analysis (ASANA). ASANA is a security system that utilizes drone technology to identify unusual and suspicious activities within a crowd. These drones fly among the crowd, streaming data via high-speed band connections to cloud servers for analysis.
Application of Drones in Security Monitoring
- - Detection of unusual behavior within a crowd from an aerial perspective.
- - Monitoring of extreme environments such as fire, flood, or natural disasters.
- - Assessment of situations and capture of visual data.
Drones require a high-speed and wireless connection due to their movement and flight patterns. The implementation of new technologies like LTE or 5G, which enhance bandwidth and reduce latency, can significantly improve accuracy and response time. Airborne devices can connect at high speeds to nearby edge computing devices, such as MEC (Multi-Access Edge Computing) systems.
Drones are increasingly being utilized in smart cities, farms, and factories. Examples include Huawei and XAG, which have supported and conducted tests on monitoring systems for smart farming. These applications employ AI-powered drones and IoT sensors, utilizing the 5G band to deploy them in agricultural environments. This solution utilizes drones in IoT applications to monitor and control farms, gathering data from video feeds and environmental sensors.
Surveillance technology ensures cybersecurity
Cybersecurity has emerged as a major concern across various industries, impacting enterprises of all sizes and individuals alike. This concern extends to the implementation of video surveillance systems.
In 2016, one of the most powerful DDoS attacks in the history of cybersecurity caused a major disruption, bringing down half of the internet in the US. This attack affected numerous service providers including Amazon, GitHub, HBO, Netflix, PayPal, Reddit, Airbnb, Twitter, and more. Over 600,000 IoT devices were infected, with a significant number being security surveillance systems.
The implications of such an attack on security surveillance systems include:
- - Increased risk to user data security.
- - Destruction of valuable surveillance video content, including critical evidence.
- - Unauthorized sale of compromised video content.
- - Unauthorized use of vulnerable video cameras as private management channels.
In the following year, 2017, following the Mirai attack, another alarming incident captured headlines. Just before US President Donald Trump's inauguration, two hackers successfully hijacked the entire video surveillance system in Washington DC. The police computer responsible for video surveillance control, as reported by WSJ, was compromised by Ransomware, granting the hackers unrestricted access to monitor the camera systems in numerous important cities across America within a few minutes.
Unfortunately, many factories manufacturing IoT devices with integrated video cameras are selling substandard products that often fail to meet security standards. The security solutions implemented in these cameras are frequently inadequate, with passwords commonly set to default and left unchanged, while Bluetooth remains constantly enabled. These technological vulnerabilities significantly ease unauthorized access to video surveillance systems.
The analysis of AI-powered video surveillance demonstrates immense potential and promising prospects for the future. AI technology, with its various applications such as image recognition and machine vision, is poised to revolutionize the field of surveillance technology.
All these components are interconnected and reliant on each other. Without the advancements in new-generation IP cameras or IoT devices, edge computing, high-speed and low-latency 4G LTE/5G bandwidth, the development of AI-based surveillance data analysis would not have reached its current state.