Open AccessA robust approach for people counting in dense crowd images using deep neural networks
Author(s) -
K Sri Harsha,
C. Shoba Bindu,
E. Sudheer Kumar
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1085/1/012010
Subject(s) - convolutional neural network , pooling , computer science , computation , artificial intelligence , pattern recognition (psychology) , layer (electronics) , deep learning , image (mathematics) , algorithm , chemistry , organic chemistry
People counting in dense crowd images with deep neural networks are proved to be effective. The models like RCNN are able to predict the crowd count by head detection, using the CNN and selective search algorithm, but these approaches are very slow, as they involve computing convolutional operation for 2k regional proposals, involving no shared computations, besides the selective search algorithm itself is slow. In this approach a Faster R-CNN for head detection which uses a Regional Proposal Network (RPN) has been used. The region proposal network is a Fully Convolutional Neural Networks that generates region proposals, these regional proposal were fed in to RoI pooling layer and subsequently classified and localized, Thus Faster-RCNN reduces computation cost of convolutional operations by passing a image only once, sharing the convolutional operations and also using RPN for regional Proposals. Using the Faster R-CNN MAE and MSE are reduced compared to R-CNN.