AI Article Synopsis
- Recent global events like the COVID-19 pandemic highlight the need for effective automatic crowd scene analysis systems to manage, count, and secure large gatherings.
- The paper reviews deep learning techniques for crowd analysis, categorizing them into two main areas: crowd counting and recognizing crowd actions.
- It also presents a new evaluation metric that assesses the accuracy of crowd counting by comparing estimated counts to actual counts in video footage.
Article Abstract
Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321087 | PMC |
| http://dx.doi.org/10.3390/jimaging6090095 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Gestalt, Navon and Kanizsa illusion processing in CVI, ADHD, and dyslexia Children with Normal verbal IQ.
Front Hum Neurosci
December 2024
Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands.
Introduction: Global Visual Selective Attention (VSA) is the ability to integrate multiple visual elements of a scene to achieve visual overview. This is essential for navigating crowded environments and recognizing objects or faces. Clinical pediatric research on global VSA deficits primarily focuses on autism spectrum disorder (ASD).
View Article and Find Full Text PDFInCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating Simultaneous Localization and Mapping in Indoor Pedestrian-Rich Spaces for Human Navigation.
Sensors (Basel)
December 2024
Institute of Computer Science, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland.
Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual-inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments. Recorded using Meta Aria Project glasses, it captures realistic scenarios without environmental control.
View Article and Find Full Text PDFMambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba.
Sensors (Basel)
December 2024
School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation.
View Article and Find Full Text PDFColor crowding considered as adaptive spatial integration.
J Vis
December 2024
Department of Neurosciences, Psychology, Drug Research and Child Health, University of Florence, Firenze, Italy.
Crowding is the inability to recognize an object in clutter, classically considered a fundamental low-level bottleneck to object recognition. Recently, however, it has been suggested that crowding, like predictive phenomena such as serial dependence, may result from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions, such as crowding being greater for nonsalient targets and, counterintuitively, that flanker interference should be associated with higher precision in judgements, leading to a lower overall error rate.
View Article and Find Full Text PDFSynchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios.
Sensors (Basel)
September 2024
Computer Engineering Department, Batangas State University, Batangas City 4200, Philippines.
In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!