Title
Scale Driven Convolutional Neural Network Model For People Counting and Localization in Crowd Scenes
Abstract
Counting and localization of people in videos consisting of low density to high density crowds encounter many key challenges including complex backgrounds, scale variations, nonuniform distributions, and occlusions. For this purpose, we propose a scale driven convolutional neural network (SD-CNN) model, which is based on the assumption that heads are the dominant and visible features regardless of the density of crowds. To deal with the problem of different scales of heads in different regions of the videos, we annotate a set of heads in random locations of the videos to develop a scale map representing the mapping of head sizes. We then extract scale aware proposals based on the scale map which are fed to the SD-CNN model acting as a head detector. Our model provides a response matrix rendering accurate head positions via nonmaximal suppression. For experimental evaluations, we consider three standard datasets presenting low density to high density crowd scenes. Our proposed SD-CNN model outperforms the state-of-the-art methods in terms of both frame-level and pixel-level analyses.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2918650
IEEE ACCESS
Keywords
Field
DocType
Convolutional neural networks,non-maximal suppression,head detection,crowd counting,motion analysis
Convolutional neural network,Computer science,Artificial intelligence,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
3
PageRank 
References 
Authors
0.41
0
3
Name
Order
Citations
PageRank
Saleh M. Basalamah18914.27
Sultan Daud Khan292.57
Habib Ullah3265.59