Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement
Arian Bakhtiarnia Qi Zhang Alexandros Iosifidis
The increasing prevalence of gigapixel resolutions has presented new
challenges for crowd counting. Such resolutions are far beyond the memory and
computation limits of current GPUs, and available deep neural network
architectures and training procedures are not designed for such massive inputs.
Although several methods have been proposed to address these challenges, they
are either limited to downsampling the input image to a small size, or
borrowing from other gigapixel tasks, which are not tailored for crowd
counting. In this paper, we propose a novel method called GigaZoom, which
iteratively zooms into the densest areas of the image and refines coarser
density maps with finer details. Through experiments, we show that GigaZoom
obtains the state-of-the-art for gigapixel crowd counting and improves the
accuracy of the next best method by 42%.