Multi-source multi-scale counting in extremely dense crowd images

We propose to leverage multiple sources of information to compute an estimate of the number of individuals present in an extremely dense crowd visible in a single image. Due to problems including perspective, occlusion, clutter, and few pixels per person, counting by human detection in such images is almost impossible. Instead, our approach re-lies on multiple sources such as low confidence head de-tections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an im-age region. Secondly, we employ a global consistency con-straint on counts using Markov Random Field. This caters for disparity in counts in local neighborhoods and across scales. We tested our approach on a new dataset of fifty crowd images containing 64K annotated humans, with the head counts ranging from 94 to 4543. This is in stark con-trast to datasets used for existing methods which contain not more than tens of individuals. We experimentally demon-strate the efficacy and reliability of the proposed approach by quantifying the counting performance. 1