The global AI in video surveillance market is expected to be valued at USD 5.6 billion in 2023 and is projected to reach USD 16.3 billion by 2028; it is expected to grow at a CAGR of 23.7% from 2023 to 2028.

In 2023, an increasing number of manufacturers are anticipated to include AI-based analytics as a standard feature in cameras and video management systems (VMS). As security cameras are now prevalent in nearly every environment, the task of manually monitoring them all has become exceedingly challenging for human operators. By incorporating AI-based analytics, security professionals gain valuable tools to effectively process and manage the vast amount of images and data they encounter.

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Opportunity: Growth in smart city initiatives

The growth in smart city initiatives presents a significant opportunity for the adoption of AI in video surveillance. Smart cities leverage advanced technologies to enhance the quality of life, improve urban infrastructure, and optimize resource management. AI-powered video surveillance systems play a crucial role in achieving these goals and addressing various challenges faced by urban environments. One key opportunity in smart city initiatives is the enhancement of public safety. AI-based video surveillance can enable real-time monitoring of public spaces, transportation systems, and critical infrastructure. AI video surveillance contributes to crime prevention, emergency response, and overall public safety by automatically detecting and alerting suspicious activities, crowd management, or traffic incidents.

Challenge: System reliability and false positives

System reliability and false positives are significant challenges in AI-based video surveillance systems. While AI algorithms can offer advanced capabilities for threat detection and behavior analysis, ensuring system reliability and minimizing false positive alerts are crucial for maintaining the effectiveness and efficiency of these systems. One challenge is the need for high accuracy and reliability in threat detection. AI video surveillance systems must be capable of detecting and identifying security threats accurately and in real time. False negatives, where a threat goes undetected, can have severe consequences. In contrast, false positives, where a benign event is flagged as a threat, can lead to alert fatigue and wasted resources. Reducing false positives is particularly critical. An excessive number of false alarms can overwhelm security personnel and reduce their ability to respond effectively to genuine threats. It can also lead to complacency or disregarding alerts, diminishing the trust and reliability of the system.