Keep Objects Centered with Object Detection-Based Camera Framing

Object centering in video frames is a valuable technique in various fields such as sports analysis, security monitoring, and wildlife tracking. The process involves dynamically adjusting the video frame to keep objects of interest centered, despite changes in their size, position, and aspect ratio. This guide outlines two main approaches: the Group Approach, which produces a single video including all objects, and the Per-Object Approach, which generates separate videos for each object. The process involves several steps, including object detection, bounding box expansion, zooming and smoothing, and fitting into the desired video resolution without distortion. By leveraging object detection models and video processing pipelines, the method allows for smooth transitions and polished results, adaptable for different applications. It emphasizes the importance of choosing the right camera and lens, as well as setting up a local inference server using tools like Roboflow Workflows and Python applications to handle object detection and video processing.