Counting and identifying characteristics of crowds can provide organizations with a lot of valuable insights. Yet challenges like image distortion, density, and different camera angles can make analyzing images accurately very challenging. Ganes Kesari, Co-founder and Head of Analytics at Gramener, joins the Intel on AI podcast to discuss how Gramener has created a crowd counting solution that can overcome those challenges and produce a very rapid and accurate analysis of images. He talks about how Gramener has utilized this solution for several AI for good projects including a joint effort with Microsoft* to count Antarctic penguin colonies. Ganes explains how their solution used convolutional neural networks (CNNs) using density-based estimations to deliver a more accurate penguin count than traditional manual counting methods.