Computer Vision in Security & Surveillance

Think about security cameras. We see them almost everywhere – on street corners, in shops, guarding buildings, and even watching our homes.

For a long time, these cameras just recorded events passively. Someone had to watch the screens to spot trouble, or review hours of footage after an incident.

Watching multiple screens makes it easy to miss things, and staring at footage all day is tiring and inefficient. What if cameras could do more than just record? What if they could understand what they see in real-time?

This is where Computer Vision comes in. Computer vision is a field of Artificial Intelligence (AI). It teaches computers to "see" and interpret the world through images and videos, similar to how humans do.

Imagine security systems that don't just record a break-in. Instead, they actively identify the intruder, detect a weapon, or alert authorities before a situation gets worse. This technology promises smarter, faster, and more reliable security.

But a key ingredient makes this possible: Data Annotation. We need to train AI models, just like we teach a child by showing them labeled pictures ("This is a cat," "This is a dog").

For computer vision in security, humans must carefully label data. They mark up countless hours of video and thousands of images.

They point out everything from faces and license plates to specific actions like climbing a fence or leaving a bag unattended.

Without this detailed labeling process, the AI cannot learn what to look for. It would just see pixels, not threats or important details.

Data annotation is the fundamental step. It transforms raw camera footage into the intelligent insights that power next-generation security systems.

What is Computer Vision in Security and Surveillance?

So, what does computer vision do for security? Essentially, it teaches computer systems to analyze visual information from cameras and other sensors.

The systems automatically detect events, identify objects or people, and understand activities.

Instead of just passively recording, these AI-powered systems act like digital watchmen. They actively analyze video feeds to recognize specific objects like cars or weapons, and identify people using facial recognition.

These systems can track the movement of individuals or vehicles across multiple cameras, understand behaviors such as suspicious loitering or sudden running, read details like license plates, and detect unusual events that deviate from normal patterns, like someone forcing a door or climbing a fence.

The goal is to automate monitoring. This makes the process more efficient and accurate than relying only on human operators. Security teams can then respond faster and more effectively to real threats.

How Is Data Annotated In Security and Surveillance?

Here’s a closer look at specific security applications and the kinds of AI models that data annotation helps train:

Object Detection and Tracking

Object Detection And Tracking

We use it to spot objects like people, vehicles, or bags in video streams and follows them as they move. This is essential for monitoring busy areas or tracking suspicious items.

Object detection often uses models like YOLO (including versions like YOLOv8, YOLOv12), SSD (Single Shot MultiBox Detector), RetinaNet, or Faster R-CNN.

To track detected objects frame-by-frame, systems often use algorithms like DeepSORT, SORT, MDNet, or various OpenCV trackers (like KCF or CSRT).

Annotators draw precise bounding boxes or polygons around objects in thousands of images. For tracking, they link the same object across multiple video frames.

This process teaches the models what objects look like and how they usually move, even if partly hidden.

Facial Recognition and Identification

We use it to identify or confirm a person's identity by analyzing their face. We use this for secure access, finding individuals, or verifying identities.

This task often uses deep learning models trained specifically on faces, such as FaceNet, VGG-Face, ArcFace, or models built using libraries like Dlib.

Annotators label faces in images. Sometimes they mark specific facial landmarks (like eye corners or the nose tip) using keypoint annotation.

Labeling faces accurately in different lighting, angles, and expressions helps train robust models that work well in real-world conditions.

Activity and Behavior Analysis

Activity and Behavioral Analysis

We use it to recognize specific actions or patterns of behavior. For example, someone fighting, running suddenly in a no-run zone, loitering suspiciously, or crowds forming or dispersing quickly.

Analyzing actions often requires models that understand sequences over time. For example 3D CNNs, which process video data, or Two-Stream Neural Networks.

Researchers also develop specialized, efficient architectures like HARNet combined with classifiers like SVM. General AI behavioral analysis systems also use various machine learning techniques.

Humans watch video clips and label segments showing specific actions. For example, they "tag" the start and end frames of a "running" sequence.

This labeling teaches the AI to tell the difference between normal activities and potentially dangerous or prohibited ones.

License Plate Recognition (ALPR)

ALPR

Automatically reads license plate characters from vehicles that cameras capture. People use this for parking management, toll roads, traffic monitoring, and law enforcement.

ALPR systems usually work in steps. First, object detection models (like YOLO or SSD) might detect the vehicle and find the plate area. Then, Optical Character Recognition (OCR) techniques read the characters. Convolutional Neural Networks (CNNs) like LPRNet often power the OCR step.

Annotators draw boxes around license plates in images. Often, they also label each individual character. This detailed labeling helps the system find and read plates accurately, even if plates are tilted, dirty, or moving fast.

Intrusion Detection

Detects when someone or something enters a restricted area without permission. This includes actions like crossing a virtual line, climbing a fence, or staying too long in a secure zone.

These systems rely heavily on object detection models (like YOLO, CNNs) to identify people or vehicles.

After detecting an object, custom rules determine if its presence or movement counts as an intrusion (e.g., a person detected inside a 'restricted zone' polygon for over 5 seconds).

Some systems might also use anomaly detection algorithms based on Deep Neural Networks (DNNs) or Recurrent Neural Networks (RNNs) to spot unusual patterns.

Annotators define the restricted zones (e.g., by drawing polygons on the camera view). They also label video frames that show actual intrusions and label the people or vehicles involved.

This process teaches the system to distinguish between normal presence near a boundary and a real security breach, which helps reduce false alarms.

In every one of these applications, the AI model's accuracy and reliability depend directly on the quality and amount of annotated data used for its training.

Companies In AI-Powered Security and Surveillance

Several companies now build advanced security solutions using computer vision. These systems actively analyze visual data from cameras to detect threats and provide useful information. Let's look at two examples:

Reconeyez

Reconeyez specializes in wireless, autonomous security systems ideal for remote sites lacking reliable power or internet, such as construction zones or border areas.

Their solution uses onboard AI to analyze visual data, classifying objects like people or vehicles and verifying potential intrusions to reduce false alarms.

The devices, powered by long-lasting batteries (up to 400 days, extendable with solar charging), communicate over a secure mesh radio network and feature PIR sensors that detect heat signatures up to 35 meters away, triggering cameras to capture evidence.

Reconeyez hardware is highly durable (IP67-rated, operational from -40°C to +60°C), and the system supports rapid deployment and remote management via a cloud platform, making it suitable for border security, critical infrastructure, and environmental monitoring.

Spot AI

Spot AI brings AI intelligence to existing business camera systems, enabling companies to extract actionable insights from standard video streams.

Their platform works with almost any IP camera brand, allowing businesses to enhance surveillance without replacing hardware.

Spot AI continuously analyzes video for specific events-such as safety incidents or unusual movements-using advanced algorithms. The unified cloud dashboard provides easy access to live and recorded footage from multiple locations, and AI-powered search and real-time alerts help users quickly find and respond to incidents.

Their Intelligent Video Recorder (IVR) processes video locally with powerful GPUs, saving bandwidth and securely backing up key clips to the cloud.

Beyond security, Spot AI delivers operational insights, such as identifying workflow bottlenecks, and the system scales easily across sites and integrates with other business software and sensors.

Challenges We Tackle in Data Annotation for Security Computer Vision at Labellerr

At Labellerr, we know that building effective AI for security and surveillance starts with top-notch annotated data. While AI promises enhanced safety, the journey to get there involves overcoming significant data preparation hurdles.

Our teams frequently encounter visual data that is far from perfect, and here’s how we navigate these tricky situations for our clients:

A major hurdle we often see is poor visibility and lighting conditions. Our clients bring us visual data from cameras operating at night or in bad weather.

Our annotators accurately draw a precise bounding box around a person or object in near-total darkness, using only grainy night-vision or thermal camera feeds.

If a person lurks in shadows or is partially hidden by fog, we meticulously identify and labels these subjects.

For instance, a client needed their AI to detect a person carrying a knife in perimeter security at night, our annotators must not only spot the person but also precisely outline the small, poorly lit weapon.

This requires extreme attention to detail, and our platform's features, like those for precise segmentation, help our teams handle such low-contrast visual data effectively.

Another common challenge we help solve is dealing with occlusion and disguised appearances. In security footage, people might wear masks, hoods, or other items that hide parts of their face or body.

If the AI's goal is to identify individuals, a mask makes this incredibly tough. Our annotation teams, following client guidelines, might flag "person with face covered" as a specific attribute.

When a system needs to detect someone acting suspiciously, like wearing a mask while loitering, our annotations capture both the mask and the activity.

We work closely with clients to establish clear labeling rules – is the mask itself a "red flag," or is it the mask combined with the behavior? Our robust QA processes ensure this consistency.

Identifying and annotating subtle but critical objects is another area where we provide expertise.

A weapon, for example, can be small, partially hidden, or only visible for a moment. We use tools like SAM to annotate the polygon with high precision, even when an object isn’t clearly defined or is held by a moving person.

Providing enough varied, accurately labeled examples of such objects is crucial for training reliable AI, and our teams are skilled in handling this demanding task.

Our platform's "CLIP Mode and Polygon Eraser" features are vital here to prevent overlapping labels when multiple threats are present.

Furthermore, security AI often needs to understand complex human behaviors and activities, not just recognize static objects.

Let's say a client needs to detect when someone is loitering around a property and repeatedly looking into windows.

This sequence of actions signals suspicious intent. To handle this, our teams perform video annotation adding attributes, labeling entire segments(Event Tagging) of video to describe the action or intent.

This is far more complex than just tagging still frames because it involves understanding context and timing.

We help clients define clear guidelines for what constitutes "suspicious loitering" versus "innocent wandering," ensuring our annotators work consistently to avoid biasing the AI model.

These nuanced scenarios are where our professional annotation team and dedication to quality shine.

At Labellerr, we understand these deep challenges in security data annotation. We combine skilled human annotators with powerful tools and strict quality checks to deliver the accurate, reliable data our clients need to build truly intelligent security systems.

Conclusion

Computer vision offers powerful tools to make security and surveillance smarter, faster, and more proactive. Systems can now analyze video feeds in real-time, identifying threats, recognizing faces, tracking objects, and understanding behavior in ways previously impossible.

However, these advanced capabilities depend entirely on high-quality data annotation. It is the essential process of labeling images and videos that teaches AI models how to "see" and interpret the world accurately.

Without careful and detailed annotation, even the most sophisticated AI algorithms will fail to perform reliably in complex, real-world security scenarios.

As computer vision technology continues to advance, the need for precise and comprehensive data annotation will only grow.

It remains the critical foundation upon which effective, intelligent security systems are built. Investing in quality annotation is investing directly in the safety and reliability of future security solutions.

FAQs

Q1: What is computer vision in the context of security and surveillance?
Computer vision refers to the use of AI and machine learning to process and analyze visual data from cameras, enabling automated monitoring, threat detection, and situational awareness in security systems.

Q2: How does computer vision enhance surveillance systems?
It allows for real-time analysis of video feeds, detecting anomalies, recognizing faces, tracking movements, and reducing the need for constant human oversight, thereby increasing efficiency and response times.

Q3: What are common applications of computer vision in security?
Applications include facial recognition for access control, intrusion detection, crowd monitoring, license plate recognition, and identifying suspicious behaviors in public and private spaces.

Q4: Are there privacy concerns associated with computer vision surveillance?
Yes, the use of facial recognition and continuous monitoring raises privacy issues. It's crucial to implement these technologies with transparency, data protection measures, and in compliance with legal regulations.

Q5: How is computer vision being used in public safety initiatives?
Public safety agencies use computer vision for monitoring large events, managing traffic, detecting emergencies, and enhancing situational awareness to respond promptly to incidents.