CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

Boominathan, Lokesh and Kruthiventi, Srinivas SS and Babu, Venkatesh R (2016) CrowdNet: A Deep Convolutional Network for Dense Crowd Counting. In: 24th ACM Multimedia Conference (MM), OCT 15-19, 2016, Amsterdam, NETHERLANDS, pp. 640-644.

PDF ACM_Mut_Con_640_2016.pdf - Published Version Restricted to Registered users only Download (2MB) | Request a copy

Abstract

Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds. We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image. Such a combination is used for effectively capturing both the high-level semantic information (face/body detectors) and the low-level features (blob detectors), that are necessary for crowd counting under large scale variations. As most crowd datasets have limited training samples (<100 images) and deep learning based approaches require large amounts of training data, we perform multi scale data augmentation. Augmenting the training samples in such a manner helps in guiding the CNN to learn scale invariant representations. Our method is tested on the challenging UCF_CC_50 dataset, and shown to outperform the state of the art methods.

Item Type:	Conference Proceedings
Additional Information:	Copy right for this article belongs to the ASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	30 Dec 2016 07:21
Last Modified:	11 Oct 2018 15:53
URI:	http://eprints.iisc.ac.in/id/eprint/55657

Actions (login required)

View Item