Jointly attention network for crowd counting

He, Yuqiang; Xia, Yinfeng; Wang, Yizhen; Yin, Baocai

doi:10.1016/j.neucom.2022.02.060

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…He and Xia et al [39] proposed a tiny CNN model called switchable speed CNN to achieve the crowd counting in embedded devices. It is simple to swap between SsCNN_A and the other two modes, SsCNN_B and SsCNN_C, which represent various trade-offs between speed and accuracy.…”

Section: A Deep Neural Networkmentioning

confidence: 99%

Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

George,

2023

JAIT

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Crowd counting system has gained significant attention in recent years due to its relevance in various domains such as urban planning, public safety, resource allocation and decision-making in crowded environments. Due to differences in crowd densities, occlusions, size changes, and perspective distortions that are frequently seen in realworld scenarios, the system, nevertheless, falls short in terms of its purpose. To address this, it is necessary to create advanced neural network architectures, efficient methods for gathering and annotating data, reliable training procedures, and assessment criteria that accurately reflect the effectiveness of crowd counting systems. Therefore, the purpose of this study is to provide a comprehensive review of the state of the art in neural network-based crowd counting systems. The developments in neural network based crowd counting procedures, along with their features and limitations, most widely datasets and evaluation criteria are explored. The experimental findings of recent crowd counting systems are also examined. Hence, this work serves as an inspiration for additional research and development in this area, ultimately advancing crowd analysis and management systems.

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Section: A Deep Neural Networkmentioning

confidence: 99%

Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

George,

2023

JAIT

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“…To mitigate the problem of huge scale variations, He et al [161] proposed a novel approach for crowd counting named Jointly Attention Network (JANet). They designed the Multi-order Scale Attention module to extract meaningful high-order statistics with abundant scale details and also introduced the Multi-pooling Relational Channel Attention module to investigate the global scope relations and structural semantics.…”

Section: Crowd Detection and Countingmentioning

confidence: 99%

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

Eyiokur¹,

Kantarcı²,

Erakin³

et al. 2022

Preprint

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The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting facehand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field.Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

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Lightweight multi-scale network with attention for accurate and efficient crowd counting

Xi,

Yan

2023

Vis Comput

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Jointly attention network for crowd counting

Cited by 10 publications

References 18 publications

Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

Lightweight multi-scale network with attention for accurate and efficient crowd counting

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