Video Analytics at Scale: Challenges and Best Practices

Video analytics involves using machine learning to automatically identify patterns and events in video content, such as detecting abnormal behavior or monitoring traffic flow. This technology has various applications, including security and surveillance, transport monitoring, healthcare, and user-generated content moderation. The main technologies behind video analytics include video processing, object detection, object recognition, and tracking. However, organizations implementing video analytics face challenges related to data drift, complexity of tools, and data storage. To address these issues, continuous training and algorithm updates are necessary, managed video solutions can be adopted, and cloud-based storage services can be utilized.