By Jay Jason Bartlett, CEO Cozaint Corp
For over a decade, the promise of intelligent video surveillance has flickered on the horizon of physical security. Systems capable of identifying faces, detecting abandoned objects, reading license plates, and counting people have existed, often grouped under the umbrella term “video analytics.” However, these early iterations, while groundbreaking for their time, frequently fell short of their potential. False positives were rampant, setup was complex, and the computational demands often strained existing infrastructure.
Today, we are seeing the trend of “A.I.” – Artificial Intelligence become more popular. However, This really isn’t just a rebranding of existing video analytics; it represents a fundamental shift in how these systems learn, adapt, and ultimately, perform. The recent surge in “AI” buzz has brought a renewed and more intense focus on these once-niche video analytics capabilities, as the underlying technology has matured significantly.
To understand this evolution, let’s demystify how these “AI-powered” video analytics systems actually work. At their core lies the concept of machine learning, a subset of AI. Instead of relying on pre-programmed rules to identify objects or behaviors, machine learning algorithms are trained on massive datasets of images and videos. Think of it like teaching a child to recognize a cat. You don’t give the child a rigid set of rules (“it has pointy ears, whiskers, and a tail”). Instead, you show them hundreds, even thousands, of pictures of cats. Over time, the child’s brain learns to identify the common patterns and features that define a cat.
AI-powered video analytics works similarly. For facial recognition, the system is fed countless images of faces, each labeled with an identity. The algorithm learns to extract unique facial features – the distance between the eyes, the shape of the jawline, the contour of the nose – and creates a digital “fingerprint” for each face. When a new face appears in the camera’s view, the system compares its features to the database of known individuals. If a close enough match is found, an identification is made.
Object left behind detection utilizes similar principles of pattern recognition. The system is trained on scenes with and without various objects. It learns to identify static objects within a defined area over a period of time. If a new object appears and remains stationary for a pre-determined duration, an alert is triggered. This could be anything from a suspicious package to a misplaced piece of equipment.
License Plate Recognition (LPR) systems focus on identifying alphanumeric characters within the specific context of a license plate. The AI algorithms are trained to locate the rectangular shape of a plate, isolate the characters, and then recognize them despite variations in lighting, angle, and plate design. This involves both object detection (finding the plate) and optical character recognition (reading the letters and numbers).
People counting systems employ various techniques, often involving the identification and tracking of human forms as they cross a virtual line or enter a defined zone. More advanced AI can differentiate between individuals moving together and accurately count even in crowded environments.
So, if these video analytics have been around for so long, what’s the big deal about “AI” now? The answer lies in the dramatic advancements in computing power, particularly the rise of Graphics Processing Units (GPUs). Originally designed for rendering graphics in video games, GPUs possess a massively parallel processing architecture. This means they can perform numerous calculations simultaneously, making them exceptionally well-suited for the computationally intensive tasks involved in training and running complex AI algorithms.
Think of a traditional Central Processing Unit (CPU) as a handful of very fast workers who can handle complex tasks one at a time. A GPU, on the other hand, is like an army of less powerful but incredibly numerous workers who can tackle thousands of simpler tasks concurrently. This parallel processing power is crucial for sifting through the vast amounts of data required to train sophisticated AI models and for processing live video feeds in real-time.
Coupled with faster CPUs and advancements in memory and data storage, this surge in processing power has led to a significant leap in the accuracy and reliability of video analytics systems. Older systems often struggled with environmental variations, poor image quality, and occlusions. AI algorithms, trained on diverse and massive datasets, are far more robust and can handle these challenges with greater precision, drastically reducing false alarms and improving the overall effectiveness of the system.
Beyond just enhanced security, the integration of AI into video surveillance is unlocking significant business intelligence opportunities. The data captured by these intelligent systems can provide valuable insights into operational efficiency, customer behavior, and resource allocation.
For example:
- Retail: People counting data can inform staffing levels, optimize store layouts, and measure the effectiveness of marketing campaigns. Facial recognition (with appropriate privacy safeguards) can personalize customer experiences and identify VIPs or potential shoplifters.
- Transportation: LPR systems can manage parking access, track vehicle flow, and optimize traffic management. Object detection can identify hazards on roadways or railway tracks.
- Manufacturing: AI-powered video analytics can monitor production lines for anomalies, ensure worker safety compliance, and track inventory movement.
- Smart Cities: These technologies can contribute to public safety, traffic flow optimization, and resource management, creating more efficient and livable urban environments.
While the foundational concepts of video analytics have existed for over a decade, the advent of powerful AI algorithms, fueled by advancements in GPU and processor technology, has ushered in a transformative era for physical security and video surveillance. These “AI-powered” systems are no longer just about detecting threats; they are becoming sophisticated tools that provide unparalleled accuracy, operational efficiency, and valuable business intelligence. As AI continues to evolve, we can expect even more intelligent, adaptive, and integrated solutions that will redefine how we protect our assets and understand our world. The “AI awakening” in physical security is not just a trend; it’s a fundamental shift towards a smarter and safer future.
About The Author:
Jay Jason Bartlett is the Managing Editor of Security.World and the CEO of Cozaint Corporation, a manufacturer of security surveillance solutions. Jay has over 40 years in the high-tech industry and over 15 years in physical security. visit: cozaint.com
Source: theailocator.com