Crowd Analysis Using Computer Vision Techniques

Jacques, Julio C. S.; Musse, Soraia Raupp; Jung, Cláudio Rosito

doi:10.1109/msp.2010.937394

Cited by 129 publications

(67 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is noted that survey papers [9], [15], [32], [33] relevant to the topic of crowd analysis have been written in the past few years. Zhan et al [15] presented a survey in 2008 on crowd analysis methods in computer vision.…”

Section: B Problems and Motivationsmentioning

confidence: 99%

“…They paid much attention on the perspectives from other research disciplines, such as sociology, psychology and computer graphics, to the computer vision approach. The paper [9] by Junior et al in 2010 also presented a survey on a wide range of computer vision techniques for crowd analysis, covering people tracking, crowd density estimation, event detection, validation and simulation. They devoted large sections to reporting how related the areas of computer vision and computer graphics should be to deal with challenges in crowd analysis.…”

Section: B Problems and Motivationsmentioning

confidence: 99%

“…video surveillance. Although many algorithms have been developed to track, recognize and understand the behaviors of various objects in video [9], they were mainly designed for common scenes with a low density of population [5], [6], [10]. When it comes to crowded scenes, the problems can not be handled well, since the large number of individuals involved not only cause the detection and tracking fail, but also greatly increase computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Under such circumstance, crowded scene analysis as a unique topic, is specifically addressed. Driven by the practical demand, it is becoming an important research direction and has already attracted lots of efforts [9], [11]- [16]. The opportunity for such study has never been better.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Crowded Scene Analysis: A Survey

Chang

Wang

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

452

201

View full text Add to dashboard Cite

Abstract-Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on crowded scene analysis have been reported, covering different aspects including crowd motion pattern learning, crowd behavior and activity analysis, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.

show abstract

Section: B Problems and Motivationsmentioning

confidence: 99%

Section: B Problems and Motivationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Crowded Scene Analysis: A Survey

Chang

Wang

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

452

201

View full text Add to dashboard Cite

show abstract

“…Although it would be of interest to develop methods for rapid analysis of video orsequential images of crowds of people to determine anomalies or whena disruption to a peaceful crowd may occur, resulting in a panicked and potentially dangerous situation, this objective has been plagued by the complexity and diversity of the problem.There are a variety of methods reported to address this problem that comprise approaches for crowd and individual modeling, crowd analysis, video analysis, and tracking individuals [8][9][10][11].However, what is lacking is a systematic approach and comparative evaluation of the analysis systems.…”

Section: Introductionmentioning

confidence: 99%

Crowd Anomaly Detection Using Standardized Modeled Input.

Long

Glade

Bierre

et al. 2012

IJIIS

View full text Add to dashboard Cite

A variety of techniques exist for tracking and detection of pedestrian traffic.The "proof of concept" or the utility of these methods is often illustrated by analysis of a video or photographs produced by the researcher as part of the development process of the algorithms.Although these images are often based on actual human subjects, they lack portability and ground truth or at best require tedious hand mapping to record ground truth.Hence, each algorithm is developed and tested for a unique situation.Consequently, as an alternative process we propose using gaming techniques to generate pedestrian and crowd like movements that readily produce ground truth referenced via data logs.For this initial study, we have used modifications of the Reynolds flocking model to generate crowd like behavior.Using these algorithms and open-source software platforms, we generated reference crowds and then added individual pedestrian behavior within the simulated crowd.Various detection methods were applied to differentcrowd scenarios to explore and assess the utility of detection methods, illustrate the possibilities of this technique, and demonstrate an initial screening for a detection algorithm.Although not a final proof of a detection process, this method allows facile, rapid, and comparative initial evaluation of the methods under consideration.

show abstract

Visual crowd analysis: Open research problems

Khan,

Menouar,

Hamila

2023

AI Magazine

View full text Add to dashboard Cite

Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep‐learning approaches have made it possible to develop fully automated vision‐based crowd‐monitoring applications. However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome. In this article, we delve into six major areas of visual crowd analysis, emphasizing the key developments in each of these areas. We outline the crucial unresolved issues that must be tackled in future works, in order to ensure that the field of automated crowd monitoring continues to progress and thrive. Several surveys related to this topic have been conducted in the past. Nonetheless, this article thoroughly examines and presents a more intuitive categorization of works, while also depicting the latest breakthroughs within the field, incorporating more recent studies carried out within the last few years in a concise manner. By carefully choosing prominent works with significant contributions in terms of novelty or performance gains, this paper presents a more comprehensive exposition of advancements in the current state‐of‐the‐art.

show abstract

Crowd Analysis Using Computer Vision Techniques

Cited by 129 publications

References 39 publications

Crowded Scene Analysis: A Survey

Crowded Scene Analysis: A Survey

Crowd Anomaly Detection Using Standardized Modeled Input.

Visual crowd analysis: Open research problems

Contact Info

Product

Resources

About