Video Surveillance System

Video surveillance systems are evolving rapidly, driven by the need for improved accuracy, efficiency, and privacy. Current research focuses on enhancing object detection and tracking through advanced algorithms like convolutional autoencoders and graph convolutional networks, often incorporating techniques like depth weighting and keypoint message passing to improve performance and reduce false positives. These advancements are impacting various applications, from automated risk detection in healthcare settings to improved livestock monitoring and more robust pedestrian detection in autonomous vehicles, while simultaneously addressing privacy concerns through anonymization strategies. The field is also exploring efficient compression methods and explainable AI frameworks to make these systems more practical and user-friendly.