Scale-Corrected Background Modeling

Abstract

Modern security cameras are capable of capturing high-resolution HD or 4K videos and support embedded analytics capable of automatically tracking objects such as people and cars moving through the scene. However, due to a lack of computational power on these cameras, the embedded video analytics cannot utilize the full available video resolution, severely limiting the range at which they can detect objects. We present a technique for scale correction, leveraging approximate camera calibration information, that uses high image resolutions in parts of the frame that are far from the camera and lower image resolution in parts of the frame that are closer to the camera. Existing background models can run on the proposed scale-normalized high-resolution (1280x720) video frame for a similar computational cost as an unnormalized 640x360 frame. Our proposed scale correction technique also improves object-level precision and recall.