  Machine learning has celebrated a lot of achievements on computer vision
tasks such as object detection, but the traditionally used models work with
relatively low resolution images. The resolution of recording devices is
gradually increasing and there is a rising need for new methods of processing
high resolution data. We propose an attention pipeline method which uses two
staged evaluation of each image or video frame under rough and refined
resolution to limit the total number of necessary evaluations. For both stages,
we make use of the fast object detection model YOLO v2. We have implemented our
model in code, which distributes the work across GPUs. We maintain high
accuracy while reaching the average performance of 3-6 fps on 4K video and 2
fps on 8K video.
