Halo - AI-Powered CCTV Object Detection
Halo is an advanced machine learning algorithm designed to analyze CCTV footage and accurately identify objects in security events. Traditional surveillance systems rely on sensor-based triggers such as radar, infrared, or microwave detection, but these often generate high volumes of false alarms. Halo enhances security monitoring by processing visual data directly from the camera, filtering out non-relevant motion and focusing on genuine threats.
Machine learning enables Halo to improve detection accuracy over time. The algorithm is trained using vast datasets containing various object types, movements, and environmental conditions. During the learning phase, the system analyzes labeled examples, distinguishing between actual security risks and common false triggers (e.g., moving branches, small animals, or light changes). Using convolutional neural networks (CNNs), Halo extracts key features from images, such as shapes, textures, and motion patterns, to identify people, vehicles, or other relevant objects accurately.
Once deployed, Halo continues to learn from real-world scenarios. Every correctly or incorrectly flagged event refines its model, adapting to site-specific conditions and reducing unnecessary alerts. If the camera provides images that meet the EN62676 standard, Halo delivers highly reliable results, significantly improving threat detection efficiency.
By integrating AI-driven video analytics with existing security infrastructure, Halo minimizes false positives while ensuring critical incidents are not overlooked. This makes it a powerful solution for high-security environments, optimizing response times and resource allocation for surveillance teams.
