CASA-Crowd: A context-aware scale aggregation CNN-based crowd counting technique

Ilyas, Naveed, Ahmad, Ashfaq and Kim, Kiseon (2019) CASA-Crowd: A context-aware scale aggregation CNN-based crowd counting technique. IEEE Access, 7. pp. 182050-182059. ISSN 2169-3536

[thumbnail of Published_Version]
PDF (Published_Version) - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview


The accuracy of object-based computer vision techniques declines due to major challenges originating from large scale variation, varying shape, perspective variation, and lack of side information. To handle these challenges most of the crowd counting methods use multi-columns (restrict themselves to a set of specific density scenes), deploying a deeper and multi-networks for density estimation. However, these techniques suffer a lot of drawbacks such as extraction of identical features from multi-column, computationally complex architecture, overestimate the density estimation in sparse areas, underestimating in dense areas and averaging of feature maps result in reduced quality of density map. To overcome these drawbacks and to provide a state-of-the-art counting accuracy with comparable computational cost, we therefore propose a deeper and wider network: a Context-aware Scale Aggregation CNN-based Crowd Counting method (CASA-Crowd) to obtain the deep, varying scale and perspective varying features. Further, we include a dilated convolution with varying filter size to obtain contextual information. In addition, due to different dilation rates, a variation in receptive field size is more useful to overcome the perspective distortion. The quality of density map is enhanced while preserving the spatial dimension by obtaining a comparable computational complexity. We further evaluate our method on three well-known datasets: UCF_CC_50, ShanghaiTech Part_A, ShanghaiTech Part_B.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 21 Dec 2021 08:30
Last Modified: 21 Apr 2023 01:18
DOI: 10.1109/ACCESS.2019.2960292

Actions (login required)

View Item View Item