3,210 research outputs found
Texture-based crowd detection and localisation
This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation
Visual Crowd Analysis: Open Research Problems
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.Comment: Accepted in AI Magazine published by Wiley Periodicals LLC on behalf
of the Association for the Advancement of Artificial Intelligenc
Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art
Growing apprehensions surrounding public safety have captured the attention
of numerous governments and security agencies across the globe. These entities
are increasingly acknowledging the imperative need for reliable and secure
crowd-monitoring systems to address these concerns. Effectively managing human
gatherings necessitates proactive measures to prevent unforeseen events or
complications, ensuring a safe and well-coordinated environment. The scarcity
of research focusing on crowd monitoring systems and their security
implications has given rise to a burgeoning area of investigation, exploring
potential approaches to safeguard human congregations effectively. Crowd
monitoring systems depend on a bifurcated approach, encompassing vision-based
and non-vision-based technologies. An in-depth analysis of these two
methodologies will be conducted in this research. The efficacy of these
approaches is contingent upon the specific environment and temporal context in
which they are deployed, as they each offer distinct advantages. This paper
endeavors to present an in-depth analysis of the recent incorporation of
artificial intelligence (AI) algorithms and models into automated systems,
emphasizing their contemporary applications and effectiveness in various
contexts
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