Why We Use Video Analytics
Historically, video surveillance relied on a person to monitor multiple cameras simultaneously and visually analyze each one to detect events. Unfortunately, this became inefficient and prone to errors because humans can only monitor so much video at a time, leading to missed key events.
Video analytics uses advanced technology to analyze video footage. Its primary purpose is to enhance security, improve operations, and provide valuable insights. Businesses and organizations benefit significantly from these tools, as they increase efficiency and help in decision-making.
What is Video Analytics? Core Concepts and Technologies
Defining Video Analytics: A Deeper Dive
Traditional CCTV cameras merely capture footage without analyzing it. In contrast, video analytics utilizes intelligent software to interpret video data. Key technologies behind this transformation include:
- Machine Learning: Systems learn from data to improve over time.
- Deep Learning: A subset of machine learning that mimics human brain processes.
- Computer Vision: Enables machines to understand visual data.
Types of Video Analytics: Object Detection, Classification, and More
Video analytics can be categorized into several types, including:
- Intrusion Detection: Alerts when someone enters a restricted area.
- Facial Recognition: Identifies individuals in real-time for security purposes.
- Loitering Detection: Notifies when someone stays in a location too long.
Real-world examples showcase the effectiveness of each type. For instance, retailers using facial recognition can track customer preferences. Meanwhile, security firms employ intrusion detection to safeguard sensitive areas.
Video analytics performance can range from very strong to very weak. Users should not trust manufacturer marketing in analytics, as it has historically made many false claims and overstated performance.
There are high-performing analytics available, with more new entrants and advancements in the past few years, which will further drive performance, but these are exceptions, not the rule.
VMD (Video Motion Detection)
Video motion detection (VMD) was the first type of method cameras used to identify movement. This particular method wasn’t powered by AI. Unlike AI, VMD analyzes basic pixel changes between frames. When a sufficient number of pixels change significantly, the camera triggers motion detected.
VMD Prone To False Alerts
However, VMD is not smart enough to know whether pixels are valid movement or not, only that they are moving. Because of this, many mistakes are made, and things like shadows, leaves, branches, animals, and others may trigger it.
Limitations in Security
The reliance on pixel changes makes VMD systems prone to misinterpretation of the scene. For example, a person walking by may trigger an alert, but the system may also falsely alert for a leaf blowing across the screen. This can lead to an overwhelming number of false alerts that require manual verification, reducing the effectiveness of the surveillance system.
The Role of AI in Improving Motion Detection
With the advent of Artificial Intelligence (AI) and machine learning (ML) algorithms, modern surveillance systems have evolved beyond VMD. AI-based motion detection systems can analyze the context of the scene, helping to distinguish between types of motion (such as differentiating between humans, animals, and environmental changes). This leads to fewer false alerts, more accurate event detection, and ultimately a more reliable surveillance system.
In summary, while VMD played a crucial role in the development of motion detection technology, it falls short in terms of accuracy and practicality, especially in environments with fluctuating conditions. AI-driven systems are now filling this gap, offering smarter, more effective solutions for modern surveillance needs.
