Artificial Intelligence in Video Surveillance

Artificial Intelligence in Video Surveillance

Global Artificial Intelligence in Video Surveillance Market to Reach US$32.3 Billion by 2030

The global market for Artificial Intelligence in Video Surveillance estimated at US$7.9 Billion in the year 2024, is expected to reach US$32.3 Billion by 2030, growing at a CAGR of 26.4% over the analysis period 2024-2030. AI in Video Surveillance Software, one of the segments analyzed in the report, is expected to record a 27.9% CAGR and reach US$12.7 Billion by the end of the analysis period. Growth in the AI in Video Surveillance Hardware segment is estimated at 22.2% CAGR over the analysis period.

The U.S. Market is Estimated at US$2.1 Billion While China is Forecast to Grow at 24.9% CAGR

The Artificial Intelligence in Video Surveillance market in the U.S. is estimated at US$2.1 Billion in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$4.9 Billion by the year 2030 trailing a CAGR of 24.9% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 24.3% and 22.6% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 18.1% CAGR.

Global Artificial Intelligence in Video Surveillance Market - Key Trends & Drivers Summarized

How Is AI Enhancing the Effectiveness of Video Surveillance?

Artificial Intelligence (AI) is transforming video surveillance systems by enhancing their ability to analyze, interpret, and respond to video footage in real time. Traditional video surveillance systems typically rely on manual review or basic motion detection, which can result in inefficiencies and missed incidents. AI, however, introduces advanced technologies such as machine learning (ML), facial recognition, object detection, and anomaly detection, making surveillance systems smarter and more responsive.

AI algorithms enable surveillance cameras to recognize specific patterns, behaviors, and objects within video feeds. For example, AI-powered systems can instantly identify suspicious activities, such as unauthorized access, loitering, or violence, and alert security personnel in real time. This proactive approach to surveillance improves the overall effectiveness of security systems, reducing response times and preventing security breaches. Additionally, AI can filter out irrelevant footage, allowing operators to focus on critical incidents, thus enhancing operational efficiency.

The integration of AI in video surveillance extends beyond security to more advanced applications. For example, in retail environments, AI can track customer behavior to optimize store layouts or improve inventory management. In transportation, AI can monitor traffic patterns and identify potential hazards, improving road safety. These applications illustrate the broader role AI is playing in revolutionizing video surveillance across various industries.

What Drives the Adoption of AI in Video Surveillance?

The growing demand for enhanced security and safety in both public and private spaces is a primary driver of AI adoption in video surveillance. As urban areas expand and organizations face increasing threats from criminal activities, terrorism, and vandalism, the need for more efficient and intelligent surveillance solutions becomes critical. AI-powered surveillance systems provide a more accurate, responsive, and scalable approach to security management.

The rise of smart cities and infrastructure is also fueling the adoption of AI in video surveillance. As cities become more connected through IoT devices and integrated technologies, surveillance systems need to manage and analyze vast amounts of data from numerous cameras and sensors. AI-powered systems enable seamless integration across city-wide networks, providing real-time insights into traffic, crowd behavior, and public safety. These systems not only improve security but also contribute to urban planning and resource management.

Regulatory pressures around public safety and the need for compliance with data privacy laws are encouraging businesses and governments to adopt AI-driven surveillance technologies. AI systems are increasingly being designed with privacy-conscious features, such as anonymizing sensitive information and ensuring secure data storage, allowing organizations to meet regulatory requirements while enhancing surveillance capabilities.

Can AI Improve Efficiency and Accuracy in Surveillance Systems?

AI is significantly improving the efficiency and accuracy of video surveillance systems by automating key functions that were once manually handled. Traditional surveillance systems often require human operators to monitor video feeds continuously, which is not only time-consuming but also prone to error and oversight. AI eliminates the need for constant human vigilance by automating the analysis of video footage, highlighting only relevant events and incidents that require attention.

In addition to real-time alerts, AI enhances the accuracy of surveillance systems by reducing false positives and minimizing human error. For example, in facial recognition applications, AI systems are capable of matching faces with high precision, even in challenging environments with poor lighting or multiple subjects. This capability is critical for security in high-traffic areas such as airports, stadiums, and shopping centers. AI systems are also able to analyze complex behavior patterns, ensuring that unusual actions, such as suspicious movements or the presence of unauthorized individuals, are detected swiftly and accurately.

AI’s ability to learn and adapt further enhances the effectiveness of surveillance systems. Over time, machine learning algorithms improve their recognition capabilities based on historical data, increasing their ability to identify patterns and anomalies. This continuous improvement ensures that AI-powered surveillance systems remain highly accurate and relevant as security needs evolve.

What’s Driving the Growth of the AI in Video Surveillance Market?

The growth in the Artificial Intelligence in Video Surveillance market is driven by several transformative factors that reflect the increasing need for intelligent, scalable, and proactive security systems. The rise in security threats, including terrorism, vandalism, and cybercrime, has pushed businesses and governments to seek advanced technologies capable of detecting and responding to incidents faster and more accurately. AI-powered video surveillance systems offer this capability, making them indispensable in modern security strategies.

Another key driver is the growing adoption of smart city initiatives, where AI is becoming an integral part of urban planning and management. In these environments, AI-powered video surveillance not only enhances security but also contributes to better traffic management, crowd control, and public safety.

The increasing integration of AI with other technologies, such as the Internet of Things (IoT), cloud computing, and edge computing, is further fueling market growth. These advancements enable AI-powered surveillance systems to process vast amounts of real-time data and offer deeper insights into public and private spaces.

Additionally, the improving affordability and accessibility of AI solutions, coupled with advancements in cloud-based platforms, are enabling smaller businesses and municipalities to adopt AI-powered surveillance systems. As the technology becomes more widespread, it is expected to permeate various sectors, from retail and transportation to healthcare and manufacturing. These factors, combined with the need for compliance with security regulations and data privacy laws, are contributing to the rapid growth of the AI in Video Surveillance market.

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence in Video Surveillance market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Component (Software Component, Hardware Component, Services Component); Deployment (On-Premise Deployment, Cloud-based Deployment); End-Use (Commercial End-Use, Residential End-Use, Industrial End-Use, Government & Public Facilities End-Use, Other End-Uses); Use Case (Weapon Detection Use Case, Intrusion Detection Use Case, Traffic Flow Analysis Use Case, Facial Recognition Use Case, Other Use Cases)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 43 Featured) -

• Axis Communications AB
• Genetec, Inc.
• Honeywell International, Inc.
• Huawei Technologies Co., Ltd.
• Milestone Systems A/S
• NEC Corporation
• Panasonic Corporation
• Sensen.ai Ltd
• Teledyne Technologies, Inc.
• VIVOTEK, Inc.

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