In this guide, we will explore the applications of computer vision in depth, with a particular focus on its use in security and surveillance.
By reading this whitepaper, you will:
Computer vision is the art of teaching a computer to see. It is a branch of artificial intelligence (AI) that focuses on enabling computers to see, observe, and understand information in the real world. Computer vision operates like human vision, except humans have a head start.
For example, it can build a model to classify whether a photo is of a cat or a dog.
As a technological discipline, computer vision seeks to apply its theories in the development of practical computer vision systems. The goal of computer vision is to develop systems that can automatically recognize, analyze, and interpret visual information to perform tasks across a wide range of applications. Computer vision is used in video surveillance, public safety, driver assistance systems in vehicles, and the automation of processes in industries such as manufacturing and logistics.
With the growth of deep learning, which allows machines to simulate human vision, enabling them to better differentiate between objects and behaviors, and increasing accuracy. Deep learning can quickly process a massive volume of video data.
Today, there is a wide range of applications for computer vision. Some of them can process image tasks, such as moderating content, monitoring security cameras, and diagnosing medical conditions. Others are quality control in manufacturing, identity validation, retail video analytics, and automated inventory analytics. In addition, it is also utilized for enhancing security and surveillance in offices or apartments by enabling real-time processing of camera feeds and automatic incident detection.
The increasing demand for using computer vision for video and image data processing has posed challenges for those businesses that use computer vision and organizations that build the solutions.
While many organizations are familiar with more granular and more regular data gathering thanks to Internet of Things (IoT) applications, image and video applications represent a considerable increase in data volumes. An image at a resolution of 2 megapixels could be as large as about 3MB in size. When streamed at 30 frames per second (FPS), even in a compressed format such as H.264, this amounts to about 10 megabits or 1.3 Gigabytes per second, or 36 Gigabits or 4 Gigabytes per hour.
Handling such large volumes of data, especially when real-time analysis is required, presents several challenges. First, all this data must be stored, which becomes increasingly complex when long-term retention is necessary for compliance, record-keeping, or legal evidence. Additionally, transmitting visual content over a network to a centralized data center can lead to bandwidth limitations and latency issues, potentially creating significant bottlenecks.
Video and real-time image processing require significant computing power, which may exceed the capabilities of existing computing resources. A legacy retail platform used for stock management often struggles to meet the growing demands of visual processing. Adding software for visual processing can increase the complexity of the solution, making operations and support more complicated.
Security is a top priority consideration for any computer system today, and capturing image or video content will only increase the need for robust security measures. For example, sensitive data could be inadvertently captured, such as video revealing a person's location at a particular point in time. In addition, some visual data is inherently sensitive, such as medical imagery, security captures, or facial recognition data.
Therefore, today, most businesses require a computer vision system capable of storing, processing, communicating, and securing visual data effectively.
The use of computer vision in security has seen remarkable growth in recent years. The market for AI in cybersecurity will reach $134 billion by 2030. The surge is being driven by the growing demand for advanced cybersecurity measures to combat sophisticated cyber threats.
Intrusion detection is one of the key applications of computer vision for security and surveillance. For instance, real-time video analysis can allow the security crew to detect unauthorized access in video footage. Not only are they more accurate, but they’re much faster than traditional methods.
Facial recognition is a big field of computer vision. It is often used in access control systems. An AI-powered system enables real-time monitoring and tracking to prevent unauthorized individuals from entering secure areas.
Additionally, computer vision is employed for increased network security. It is used for detecting anomalies, providing us with valuable insights, and taking a proactive approach to prevent breaches before they occur.
Generally, computer vision is changing the way we think about safety and surveillance. With continuous advancements in AI and machine learning, the future of computer vision for security and surveillance looks promising.
Utilizing computer vision for security and surveillance serves various purposes for a wide range of businesses and organizations, such as recognizing objects & faces, identifying the license plates of vehicles that break the traffic laws and etc. Here are some of the top applications of computer vision for security and surveillance:
Computer vision is a powerful solution for supporting surveillance and monitoring objects such as people and vehicles. It is widely used across various industries, for example, identifying suspicious individuals and preventing theft in security, or detecting defects in manufacturing. This versatile technology is effective in both public and private spaces, making it ideal for spotting unusual activities or objects.
Facial recognition is one of the most prominent applications of computer vision for security and surveillance. It can detect an individual's identification based on unique facial patterns. This technology offers numerous benefits, such as enhancing access control, locating suspects, and verifying identities.
Anomaly detection is another key application of computer vision in security & surveillance. This feature helps identify unusual patterns in live video feeds, such as unattended objects or suspicious behavior in specific contexts. AI-powered systems can proactively raise alerts before situations escalate, enhancing overall security response. It is widely used in high-security environments like banks, airports, and other sensitive areas.
Video content analysis (VCA) is a well-established use case of computer vision for security and surveillance. It automates the processing of live or recorded footage, significantly enhancing real-time monitoring. By detecting patterns and motion, VCA also provides valuable insights for post-event investigations. Many businesses and organizations rely on this technology to improve security and streamline workplace management.
Computer vision can track and interpret pedestrian patterns through people movement analysis in various environments such as malls, airports, and other public spaces. These movement patterns are collected and stored as part of large datasets, which can be utilized for multiple purposes such as Government agencies frequently use this technology to manage large gatherings, such as concerts and major public events.
One of the most revolutionary use cases of computer vision for security and surveillance is understanding human behavior. Computer vision technology is leveraged to analyze posters, gestures, and communication that happens among humans in different layouts. It is widely used to prevent conflicts and maintain decorum in public places like hospitals, research institutes, and educational organizations.
Computer vision is used for tracking theft, detecting unauthorized access, and identifying illegal activities. It can recognize suspicious movements and behaviors, making it an effective tool in security and surveillance. As a result, it is widely adopted to prevent shoplifting in retail stores and to detect criminal activities in urban areas.
There are key benefits when utilizing computer vision technology in Security and Surveillance, including:
Computer vision is transforming the landscape of security, public safety, and data analysis. The integration of computer vision into CCTV systems represents a significant evolution in surveillance technology. Computer vision enables CCTV systems to recognize faces, track movements, and even detect anomalies without human intervention. For example, a computer vision-enhanced camera feed could actively alert authorities to unattended luggage or identify a person. In other words, CCTV cameras are not just watching, they are analyzing and making decisions
Sky Solution’s client wanted to develop a system for CCTV in their building. Their existing system could not handle a high-volume workforce and often failed to verify employees accurately. With multiple cameras feeding into the system, slow processing speeds and missed identifications led to bottlenecks, security gaps, and mounting administrative headaches.
The new system is expected to enhance security, streamline access management, and improve operational efficiency.
Demanded features for the AI-driven CCTV system:
The AI-driven CCTV system developed by Sky Solution has come up with these main features:
Our system streamlines the enrollment process by requiring just three still images of a person, captured from the front and both side profiles, to register them into the Video Management System (VMS). Additionally, the system supports fine-tuning facial recognition models using human datasets specific to a geographical region. This helps significantly enhance recognition accuracy.
This advanced system enables real-time tracking of multiple identities across the coverage areas of various cameras within a building. It captures and traces the movement trajectories over a specified period, providing valuable insights into their activity and location history. The current positions of all tracked identities are displayed dynamically on a 2D floor plan of the building, offering a clear, real-time visualization of movement patterns and enhancing situational awareness across the premises.
The system enables operators to locate individuals across video footage using facial recognition technology efficiently. By uploading an image of a person's face, the operator initiates a search, and the system returns a list of video segments where that individual appears. The standard search period spans up to one day, but this duration can be extended depending on the available hardware configuration, allowing for flexible and scalable performance.
When an operator identifies a stranger in a video scene, they can select and crop the ROI containing the person’s body and input it into the system’s search engine. The system will then analyze the provided data and return a list of video footage where the same individual appears. The standard search time spans up to one day, with the option to extend it depending on the hardware configuration available.
Our system enables efficient check-in through advanced facial recognition technology, allowing employees to access the premises quickly and securely. It integrates seamlessly with door and lock mechanisms via API, automating the process of entry and exit. Upon successful check-in or check-out, staff members receive instant email notifications, enhancing transparency and accountability. At the end of each day, a comprehensive check-in/check-out report for the entire company is automatically generated and sent to the operator, ensuring accurate tracking and streamlined management of attendance data.
To enhance security, the system is equipped with the capability to detect individuals whose facial data is not enrolled in the existing database. When an unrecognized face is identified, the system triggers real-time alerts, allowing security teams to respond promptly to potential intrusions. This proactive feature helps prevent unauthorized access, strengthens perimeter protection, and ensures that only verified personnel can enter secured areas.
For companies operating across multiple buildings or floors in different locations, centralized oversight can be a challenge. Our Video Management System (VMS) offers a unified solution, enabling seamless monitoring and control of all sites from a single platform. Whether it's separate offices in different cities or multiple departments across various floors, our VMS ensures consistent security, streamlined operations, and efficient management—all in one place.
The system supports two primary roles: admin and staff. This structure is designed to be flexible and can be easily extended to accommodate additional roles based on customer requirements, ensuring tailored access control that meets diverse organizational needs.
OpenCV is a great-performing computer vision tool and can work well with C++ as well as Python. It is used to perform several image and video processing tasks. It’s quite easy to use even if you are a beginner. It has C++, Python, Java, and MATLAB interfaces and works on multiple platforms, allowing you to build applications for Linux, Windows, and Android. Despite that, it gets a bit slow working through massive data sets or very large images. Additionally, it doesn’t have GPU support and relies on CUDA for GPU processing.
Key features of OpenCV include:
OpenCV can work seamlessly with TensorFlow, PyTorch, and other deep learning frameworks to enhance AI-driven vision applications. Especially, it is a good fit for mass-produced products.
TensorFlow is an open-source machine learning framework developed by Google Brain. It is widely used for building, training, and deploying artificial intelligence models, especially in deep learning. It can run on tiny CPUs or microcontrollers, and can scale up to multiple GPUs or run on tensor processing units.
Key components of TensorFlow for computer vision include:
CUDA (Compute Unified Device Architecture) is NVIDIA’s parallel computing platform and programming model that enables developers to use GPUs (Graphics Processing Units) for general-purpose computing, including computer vision and machine learning tasks. By allowing programs to run directly on the GPU, CUDA takes advantage of thousands of cores for massively parallel processing. Its architecture also supports efficient memory management and fast data transfer between the CPU and GPU, crucial for high-performance computer vision applications. Notably, CUDA can accelerate image processing tasks by up to 100 times compared to CPU-only implementations.
C++ is a programming language that supports procedural, object-oriented, and generic programming. It is statically typed, compiled, case-sensitive, and designed for general-purpose use with a flexible, free-form syntax. By combining both high-level and low-level language features, C++ offers a powerful balance of performance and abstraction.
For example, using C++ to process a 4K video stream at 60 FPS eliminates the overhead of Python's interpreter, resulting in lower latency. However, achieving the same in C++ involves managing pointers, handling compilation dependencies, and debugging segmentation faults—challenges that can be daunting for beginners.
Python is among the most popular computer vision tools. It is widely used in software applications, web pages, and games. It is also used in scientific and mathematical computing and AI projects.
Advantages of using Python for computer vision
PyTorch is an open-source machine learning framework. It is useful because it contains a lot of the core building blocks that you might need to implement deep learning models, whether you’re doing natural image processing, computer vision, audio processing, or more.
PyTorch offers a flexible and user-friendly framework for building and training deep learning models, making it well-suited for tasks like image classification, object detection, and image segmentation.
Key features of PyTorch include:
Computer vision is transforming the way we approach security and surveillance, making systems smarter, faster, and more reliable. From real-time facial recognition to multi-camera tracking and intrusion detection, the technology offers powerful tools to enhance safety across various environments. By leveraging computer vision for security and surveillance, businesses can efficiently manage access control, detect risk early, and transform data into opportunities.
At Sky Solution, we are proud to be at the forefront of innovation, integrating Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision into our advanced security solutions. Our platform goes beyond detecting anomalies; it anticipates them, delivering tailored, predictive security measures that evolve with your unique business needs.
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