Surveillance Application

Surveillance applications leverage computer vision and machine learning to automate tasks like object detection, tracking, and identification within video footage. Current research emphasizes improving the robustness of these systems to real-world challenges, such as variations in lighting, weather, and image quality, often employing deep learning models and advanced fusion techniques to integrate data from multiple sensors (e.g., visible and infrared cameras). This field is crucial for enhancing security and safety in various settings, from public spaces to industrial facilities, while ongoing research addresses limitations in accuracy, scalability, and resilience to adversarial attacks.