Abstract:
People counting in extremely dense crowds is a challenging problem due to severe occlusions, few pixels per head, cluttered environments and skewed camera perspectives. In this thesis, we present a novel algorithm for people counting in highly dense crowd images. Our approach relies on the fact that head is the most visible part of an individual in a dense crowd. As such, a head detector can be used to estimate the spatially varying head size, which is the key feature used in our head counting procedure. We leverage the state-of-the art convolutional neural network for the sparse head detection in dense crowd. After sub-dividing the image into rectangular patches, we first use a SURF feature based SVM binary classifier to label each patch as crowd/not-crowd and eliminate all not-crowd patches. Regression is then performed on each crowd patch to estimate average head size. The number of individuals in each patch is estimated by dividing the patch area with the estimated head size. For the crowd patches where no heads are detected, the counts are estimated based on distance based weighted averaging over the counts from neighboring patches. Finally, the individual patch counts are summed up to obtain the total count. We evaluate our approach on three publicly available datasets for extremely dense crowds: UCF_CC_50, ShanghaiTech, and AHU-Crowd. Our approach gives comparable results on these challenging datasets to other state of the art algorithms, but unlike other algorithms, our proposed method does not require the laborious task in obtaining labeled training data of dense crowd images.
