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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.

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Official URL: http://dx.doi.org/10.1145/2964284.2967300

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
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Additional Information: Copy right for this article belongs to the ASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
Department/Centre: Others
Date Deposited: 30 Dec 2016 07:21
Last Modified: 30 Dec 2016 07:21
URI: http://eprints.iisc.ernet.in/id/eprint/55657

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