AI is transforming the way we interact with our cities. The global smart cities market size accounts for USD 1.82 billion in 2024 and is predicted to reach around USD 13.95 billion by 2034, growing at a CAGR of 22.59% from 2024 to 2034.
The AI vision abilities introduce a more efficient and sustainable way to improve citizens' security experiences within cities with video analytics. Video analytics is a computer vision application that enables a system to automatically analyze, detect, and determine spatial events in a video. Smart cities produce a myriad of video data. Leveraging AI video analytics enhances the smart city surveillance system’s ability to interpret and understand visual data. It presents other benefits, from real-time criminal behavior identification to traffic coordination and accident prevention.
In this article, we’ll discuss the importance of AI video analytics in smart cities and highlight some of its surveillance use cases.
Why is video analytics critical for surveillance in smart cities?
Smart city surveillance systems typically use IP cameras and CCTVs to monitor events in real-time, and manually reviewing the video data collected from cameras is tedious and monotonous for security personnel or operators. Employing AI video analytics mitigates manual video data review errors and enables real-time identification of complex behaviors, events, and activities from this constantly generated data. It facilitates real-time status updates on occurring events that can be used to send out alarms about crimes or warnings about accidents, increasing the chances of catching the perpetrators or sending quick help to victims. it also.
Use cases of AI-powered video analytics for surveillance
The applications of AI-powered video analytics in surveillance primarily enhance urban security and safety through automated and precise monitoring functions. These applications empower smart city surveillance systems to monitor, analyze, and respond to potential security threats in real time, ensuring proactive and efficient safety measures.
INTRUSION DETECTION
Identifying a person's or vehicle's intent when entering restricted areas, such as government facilities, critical infrastructure, and commercial or private premises, is challenging because it is difficult to distinguish between harmless and suspicious activity in real time. Intelligent surveillance systems determine suspicious activities by flagging unauthorized personnel or entry during off-hours. These systems alert concerned authorities or security teams for immediate action, reducing response time and enhancing security in sensitive areas.
LOITERING DETECTION
Monitoring people lingering in specific locations around a city can indicate potential security risks in places such as subway stations, airports, and public squares. Some surveillance systems are designed to set time thresholds to identify individuals who remain in a specific area for an unusual duration, potentially signaling suspicious behavior. When unusual loitering is detected, alerts are generated for security personnel to investigate, helping prevent vandalism, loitering-related theft, and other potential security issues.
ILLEGAL CAR PARKING
This involves recognizing vehicles parked in unauthorized zones, such as fire lanes, handicapped spaces, or loading zones. These detections help city management and law enforcement respond to parking violations more efficiently. They also aid in maintaining smooth traffic flow by ensuring vital pathways remain unblocked and accessible.
PEOPLE COUNTING
Estimating the population density across specific areas in a city, particularly in public venues, event spaces, and transport hubs, enables authorities to manage large gatherings more effectively. They can implement crowd control measures and ensure public safety during events if necessary. It also aids in analyzing urban space usage, helping to improve infrastructure planning and optimize resource allocation.
Privacy Concerns and Ethical Considerations
Deploying AI-powered video analytics in smart cities can greatly improve public safety, yet it raises substantial privacy and ethical concerns. Continuous monitoring of public spaces can infringe on citizens' rights to privacy, often without their explicit consent. The vast amount of personal data collected—including movement patterns and behavior—poses risks of misuse or unauthorized access, making strong data protection and secure management critical.
This is why data security is critical. The vast amounts of video data collected require stringent security measures to prevent unauthorized access, breaches, or misuse. To ensure secure data handling, smart cities should establish strict data access policies, retention limits, and secure deletion practices once data is no longer necessary. Additionally, there is a risk of algorithmic bias, where surveillance systems may unintentionally target certain demographics. Regular audits and ethical AI practices are necessary to mitigate this risk and ensure fair, unbiased surveillance
Ethical deployment of video analytics also requires clear communication and adherence to data protection laws. Cities should inform residents about what data is collected, its usage, and how privacy is safeguarded. Aligning with current privacy standards, such as the GDPR in Europe, and anticipating future regulations will further promote ethical, transparent AI use. Through secure data management, regulatory compliance, and proactive community engagement, cities can enhance security while respecting citizen privacy rights.
Developing your video analytics surveillance solution with an end-to-end CVOps platform
The major technical development challenge of a video analytics surveillance system for smart cities revolve around handling it’s complex workflows and processes, from managing the continuously generated video data to regular model retraining current for accuracy. These development workflows and processes typically have varied requirement in terms of tools, infrastructure and platform which can lead to workflow bottlenecks. The development of a computer vision systems may require juggling multiple tools and workflows, which can lead to data silos and inconsistent processing of the surveillance data. MLOps platforms streamline your development and production workflows and processes by ensuring smooth integration, versioning, and monitoring across across the system.
An end-to-end computer vision based MLOps platform like Picsellia offers comprehensive advantages for developing video analytics surveillance systems. It provides a centralised platform for seamless handling your surveillance data and models from start to finish. It also reduces the development friction of using different tools or platforms to manage them. Picsellia ensures continuity in data tracking and streamlines model updates, helping city authorities create robust, real-time surveillance solutions for complex urban environments.
Beyond centralization, Picsellia’s CVOps platform also enhances collaboration, monitoring, and scalability. Its intuitive interface supports multiple ML frameworks, making it easier for diverse teams to develop and deploy models. In the context of smart city surveillance, this means cities can monitor for model drift, adjust for anomalies, and stay compliant with privacy regulations—all within one platform. Overall, Picsellia provides an adaptable and resilient approach to building video analytics solutions, helping city surveillance teams to maintain high accuracy, ensure rapid response, and enforceing citizen data privacy compliance regulations by allowing them adopt any required architecture infrastructure setups.
Discover how Picsellia can enable the development of your AI video analytics surveillance system, book a demo today!
.png)
.png)
