Crowd Counting in a Highly Congested Scene using Deep Augmentation Based Convolutional Network

9 Pages Posted: 24 May 2019

See all articles by Nirbhay Kumar Tagore

Nirbhay Kumar Tagore

Indian Institute of Technology(BHU)

Shashank Kumar Singh

Indian Institute of Technology(BHU)

Date Written: March 15, 2019

Abstract

In this Paper, we proposed a unique two-level data augmentation-based approach for crowd counting in a still image. Most of the existing researchers have used deep and shallow two different types of networks for high and low-level feature extraction. We used a single framework for high and low-level feature extraction. In our approach, we first convert our training samples to polar coordinates and use the magnitude of the resultant image and the skeleton of the image to feed directly to the deep convolutional neural network with the original training samples. Most of the crowd datasets have very less amount of training samples and deep learning-based approaches require large amounts of training data. This approach of two-level augmentation helps our deep network to learn high-level features directly from original samples as well as the low-level features from augmented samples. This feature extraction is then followed by a fully connected regress network for local patch count. We used two public datasets to demonstrate our proposed approach, UCF, and Shanghaitech datasets. The experiments show that our deep augmentation-based approach outperforms most of the state of the art methods.

Keywords: Deep Augmentation, Convolutional Neural Network, Crowd Counting

Suggested Citation

Tagore, Nirbhay Kumar and Singh, Shashank Kumar, Crowd Counting in a Highly Congested Scene using Deep Augmentation Based Convolutional Network (March 15, 2019). International Conference on Advances in Engineering Science Management & Technology (ICAESMT) - 2019, Uttaranchal University, Dehradun, India, Available at SSRN: https://ssrn.com/abstract=3392307 or http://dx.doi.org/10.2139/ssrn.3392307

Nirbhay Kumar Tagore (Contact Author)

Indian Institute of Technology(BHU) ( email )

Varanasi
India

Shashank Kumar Singh

Indian Institute of Technology(BHU) ( email )

Varanasi
India

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