CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

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Authors R. Venkatesh Babu, Lokesh Boominathan, Srinivas S S Kruthiventi
Journal/Conference Name MM 2016 - Proceedings of the 2016 ACM Multimedia Conference
Paper Category
Paper 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.
Date of publication 2016
Code Programming Language Multiple

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