Crowd Density Estimation Using Fusion of Multi-Layer Features

Crowd counting is very important in many tasks such as video surveillance, traffic monitoring, public security, and urban planning, so it is a very important part of the intelligent transportation system. However, achieving an accurate crowd counting and generating a precise density map are still ch...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2021-08, Vol.22 (8), p.4776-4787
Main Authors: Ding, Xinghao, He, Fujin, Lin, Zhirui, Wang, Yu, Guo, Huimin, Huang, Yue
Format: Article
Language:English
Subjects:
Artificial neural networks
Coders
Crowd counting
Crowd monitoring
Datasets
Decoding
Density distribution
density map
encoder-decoder
Encoders-Decoders
Estimation
Feature extraction
Feature maps
fusion
Head
Intelligent transportation systems
Kernel
Multilayers
Occlusion
Semantics
Task analysis
Task complexity
Traffic planning
Traffic surveillance
Urban planning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Crowd counting is very important in many tasks such as video surveillance, traffic monitoring, public security, and urban planning, so it is a very important part of the intelligent transportation system. However, achieving an accurate crowd counting and generating a precise density map are still challenging tasks due to the occlusion, perspective distortion, complex backgrounds, and varying scales. In addition, most of the existing methods focus only on the accuracy of crowd counting without considering the correctness of a density distribution; namely, there are many false negatives and false positives in a generated density map. To address this issue, we propose a novel encoder-decoder Convolution Neural Network (CNN) that fuses the feature maps in both encoding and decoding sub-networks to generate a more reasonable density map and estimate the number of people more accurately. Furthermore, we introduce a new evaluation method named the Patch Absolute Error (PAE) which is more appropriate to measure the accuracy of a density map. The extensive experiments on several existing public crowd counting datasets demonstrate that our approach achieves better performance than the current state-of-the-art methods. Lastly, considering the cross-scene crowd counting in practice, we evaluate our model on some cross-scene datasets. The results show our method has a good performance in cross-scene datasets.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.2983475