Table 1 Summary of previous reviews
From: A survey of deep learning methods for density estimation and crowd counting
# | Title | Year | Venue | Brief description |
|---|---|---|---|---|
1 | Crowd analysis: a survey [24] | 2008 | MVA | This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach |
2 | Crowd analysis using computer vision techniques [68] | 2010 | ISPM | A survey on crowd analysis by using computer vision techniques, including different aspects such as people tracking, crowd density estimation, event detection, validation and simulation |
3 | A Survey of Human-Sensing:Methods for Detecting Presence, Count, Location, Track, and Identity [69] | 2010 | ACM Computing Surveys | a survey of the inherently multidisciplinary literature of human-sensing , focusing mainly on the extraction of five commonly needed spatio-temporal properties: namely presence, count, location, track and identity |
4 | Crowd counting and profiling: Methodology and evaluation [70] | 2013 | MSVAC | This study describes and compares the state-of-the-art methods for video imagery based crowd counting, and provides a systematic evaluation of different methods using the same protocol |
5 | Performance evaluation of crowd image analysis using the PETS2009 dataset [71] | 2014 | PRL | This paper presents PETS2009 crowd analysis dataset and highlights detection and tracking performance on it |
6 | Crowded scene analysis: A survey [72] | 2015 | TCSVT | This paper surveys the state-of-the-art techniques on crowded scene analysis with different methods such as crowd motion pattern learning, crowd behavior, activity analysis and anomaly detection in crowds |
7 | An evaluation of crowd counting methods, features and regression models [73] | 2015 | CVIU | This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models |
8 | Recent survey on crowd density estimation and counting for visual surveillance [74] | 2015 | EAAI | This paper presents a survey on crowd density estimation and counting methods employed for visual surveillance in the perspective of computer vision research |
9 | Advances and trends in visual crowd analysis: A systematic survey and evaluation of crowd modelling techniques [75] | 2016 | Neurocomputing | This paper aims to give an account of such issues by deducing key statistical evidence from the existing literature and providing recommendations towards focusing on the general aspects of techniques rather than any specific algorithm |
10 | Crowd scene understanding from video: a survey [76] | 2017 | TOMM | This survey explores crowd analysis as it relates to two primary research areas: crowd statistics and behavior understanding |
11 | A survey of recent advances in cnn-based single image crowd counting and density estimation [77] | 2018 | PRL | A review of various single image crowd counting and density estimation methods with a specific focus on recent CNN-based approaches |
12 | Convolutional neural networks for crowd behaviour analysis: a survey [78] | 2019 | VC | A survey for crowd analysis using CNN |
13 | Approaches on crowd counting and density estimation: a review [79] | 2021 | PAA | A review for the traditional crowd counting methods and the procedure of their development |
14 | A survey on deep learning-based single image crowd counting: Network design, loss func-tion and supervisory signal [80] | 2022 | Neurocomputing | A survey for the three major design modules for crowd counting |
15 | Revisiting crowd counting: State-of-the-art, trends, and future perspectives [81] | 2023 | IVC | A review for the most significant contributions by model architectures, learning methods and evaluation methods |