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Switching Convolutional Neural Network for Crowd Counting

Sam, Deepak Babu and Surya, Shiv and Babu, RV (2017) Switching Convolutional Neural Network for Crowd Counting. In: 30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), JUL 21-26, 2016, Honolulu, HI, pp. 4031-4039.

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Official URL: http://dx.doi.org/10.1109/CVPR.2017.429


We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera view-points. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures, recurrent networks and late fusion of features from multi-column CNN with different receptive fields. We propose switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count. Patches from a grid within a crowd scene are relayed to independent CNN regressors based on crowd count prediction quality of the CNN established during training. The independent CNN regressors are designed to have different receptive fields and a switch classifier is trained to relay the crowd scene patch to the best CNN regressor. We perform extensive experiments on all major crowd counting datasets and evidence better performance compared to current state-of-the-art methods. We provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch. It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.

Item Type: Conference Paper
Series.: IEEE Conference on Computer Vision and Pattern Recognition
Publisher: 10.1109/CVPR.2017.429
Additional Information: 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, JUL 21-26, 2016 Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 20 Jan 2018 05:41
Last Modified: 25 Feb 2019 05:48
URI: http://eprints.iisc.ac.in/id/eprint/58845

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