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Explore how Ultralytics YOLO11 is redefining real-time security monitoring with AI by improving live threat detection and enabling smarter surveillance.
Smart surveillance technologies play a vital role in protecting people, property, and infrastructure across the world. At the heart of these efforts are camera systems, which monitor streets, airports, schools, offices, and public spaces around the clock. With over a billion surveillance cameras in use globally, the amount of recorded video is growing faster than ever.
Traditionally, reviewing this footage has been a manual task handled by human operators scanning screens for potential threats. While this approach can work in smaller settings, it becomes overwhelming and inefficient at larger scales. It’s also time-consuming, which is a major drawback in fast-moving or crowded environments.
Today, video surveillance systems are starting to rely on artificial intelligence (AI) solutions to provide real-time insights to make more informed decisions. A key part of this progress is computer vision, a branch of AI that allows machines to interpret visual data.
Computer vision models like Ultralytics YOLO11 are designed to handle various real-time image and video detection tasks. They can detect individuals, track movement, and spot unusual behavior with speed and accuracy. Even in complex environments, such models enable security teams to stay alert and responsive.
In this article, we’ll explore how computer vision and models like YOLO11 can help to change the way security is managed across different environments. Let’s get started!
The role of computer vision and AI in public safety systems
The security industry is quickly embracing computer vision. Smart surveillance systems that combine computer vision, edge computing (which processes data locally, near the source), and CCTV cameras can now analyze people and vehicles in real-time, helping security teams detect threats more efficiently. As AI and camera technologies continue to advance, video analysis is becoming nearly as sharp as the human eye, reshaping how we safeguard public spaces.
Computer vision systems can perform tasks such as detecting objects, tracking movement, and recognizing patterns in videos. This means they can identify people, detect unusual behavior, and monitor activity as it happens. Such capabilities can make surveillance systems more advanced and reliable in both public and private spaces. As a result, the AI video surveillance market is expected to grow to $12.46 billion by 2030.
Fig 1. The role of computer vision in security systems. Image by author.
How Ultralytics YOLO11 can enable smarter security systems
Next, let’s take a closer look at Ultralytics YOLO11 and the features that make it an impactful tool for real-time video analysis.
Built on recent advances in AI and computer vision, Ultralytics YOLO11 offers faster processing, higher accuracy, and greater flexibility for applications like video-based security systems.
Similar to previous YOLO models, YOLO11 can handle complex Vision AI tasks such as object detection (locating and identifying objects), instance segmentation (highlighting and outlining specific objects in an image), object tracking (following objects over time), and pose estimation (understanding how objects are positioned or moving).
YOLO11 is also far more efficient than earlier models. With 22% fewer parameters than Ultralytics YOLOv8m, it achieves a higher mean average precision (mAP) on the COCO dataset, meaning YOLO11m detects objects more accurately while using fewer resources. On top of this, it delivers faster processing speeds, making it well-suited for real-time applications where rapid detection and response are critical and every millisecond counts.
Fig 2. YOLO11 outperforms previous YOLO models across various benchmark tests.
Using YOLO11 and computer vision for security applications
Now that we have a better understanding of how computer vision works in security and surveillance systems, let’s take a closer look at some real-world security applications where YOLO11 can play a key role.
Intrusion detection using computer vision and YOLO11
Keeping restricted areas secure is essential for ensuring safety and protecting property. Whether it’s a private site, warehouse, or public transport facility, detecting unauthorized access can prevent serious incidents.
YOLO11 can help with real-time intrusion detection by identifying people, vehicles, or other moving objects through video feeds. Within the camera’s view, virtual boundaries called geo-fences can be defined. When an object crosses into a restricted zone, YOLO11 can detect the intrusion and trigger an alert or pass the detection data to an integrated security system for further action.
Detected objects are highlighted with bounding boxes, providing a clear visual indication of activity. It reduces the need for continuous human monitoring and increases the chances of catching incidents as they occur.
This approach is also useful in public safety settings. For instance, yellow lines on train platforms indicate areas that passengers should not cross for safety reasons. In such scenarios, YOLO11 can be used to monitor the boundary line and detect when someone steps past it. The system can then change the color of the bounding box to highlight a potential safety concern. With capabilities like this, YOLO11 enables more responsive and reliable intrusion detection in high-risk environments.
Abandoned object detection in surveillance with YOLO11
An unattended bag in a busy airport or train station can quickly raise security concerns. In crowded public spaces, it’s challenging for security personnel to spot such objects quickly, especially during long shifts or peak hours. Delays in detection can lead to unnecessary panic or safety risks.
Computer vision models like YOLO11 can help improve surveillance by detecting, segmenting, and tracking unattended objects in real-time video feeds. If a bag or package is identified as remaining stationary in one place for too long without a person nearby, the system can flag it as potentially abandoned. This added layer of analysis can distinguish objects more accurately and reduce the need for constant human observation, enabling faster and more focused responses.
Fig 3. Using YOLO11 to detect a suitcase.
Entry and exit counting with AI models like YOLO11
Knowing how many people are entering and exiting a space is vital for both safety and operational efficiency. In places like shopping malls, office buildings, and train stations, this information can streamline managing large crowds, improving layouts, and keeping daily operations running smoothly.
Before the adoption of computer vision, counting was typically done by staff using clickers or simple sensors at the door. Such methods work, but they’re not efficient when facing larger crowds. They are also not always reliable when dealing with facilities having multiple entrances and exits.
YOLO11’s support for object detection and tracking can be used to count people or objects within a defined region of interest. It can help count entries and exits in real time, even when facing large or crowded spaces. For example, retail stores can use this method to track foot traffic across multiple entry points, assisting managers in adjusting staffing during peak hours.
Accurate entry and exit data can also support long-term planning. The insights from such data can aid managers in studying foot traffic patterns over time, making it possible for them to identify high-traffic zones and decide where to place signs or reconfigure entrances to improve comfort and safety.
Fig 4. An example of a real-time entry and exit counter enabled by YOLO11.
Pros and cons of AI-powered surveillance systems
Here are some of the key benefits of using computer vision in smart security systems:
Cost efficiency over time: While the initial setup may be costly, AI systems can reduce long-term expenses related to staffing, training, and operational inefficiencies.
Scalability: AI surveillance solutions are easily scalable, making them suitable for anything from a small office to a large city-wide camera network.
Easy integration with existing infrastructure: Many AI models, including YOLO11, are designed to integrate seamlessly with current CCTV and security systems, minimizing disruption.
Despite the various advantages of AI-powered surveillance, there are also some limitations to keep in mind. Here are a few key challenges associated with smart surveillance systems:
Ethical and privacy concerns: While using computer vision in public spaces, it’s important to address questions related to consent, data storage, and how footage is handled to ensure privacy is respected.
Dependence on quality training data: The performance of computer vision models depends heavily on well-curated, diverse datasets. Poor or biased training data can lead to inaccurate detection, misidentification, or discriminatory outcomes.
Environmental factors: Factors like poor lighting, weather, or visual obstructions can affect detection performance, particularly outdoors.
Key takeaways
YOLO11 is improving real-time security solutions by helping detect people, objects, and unusual activity with greater speed and accuracy. It supports applications like intrusion detection, object tracking, and loitering alerts, making it useful in public areas, workplaces, and transportation hubs.
By reducing the need for constant manual monitoring, YOLO11 allows security teams to respond faster and more confidently. Its ability to handle crowd analysis and people counting shows how Vision AI is shaping the future of safety. As technology advances, it will likely continue to support smarter, more reliable surveillance systems.
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