Analyzing the Crowd Evacuation Pattern of a Large Densely Populated Building

Alginahi, Yasser M.; Mudassar, Mohammed; Kabir, Muhammad Nomani; Tayan, Omar

doi:10.1007/s13369-018-3411-z

Cited by 17 publications

(7 citation statements)

References 25 publications

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“…The accuracy achieved for both data sets are 98% and 88%, respectively. A software named as legion Evac has been used in [47] for the behavior of crowd evacuation, during simulating legion Evac calculates various metrics that reflect a holistic pattern of crowd evacuation, which capture the behavior of crowd. In [85], authors have developed a probabilistic detection of crowd events (running, walking, splitting, merging, local dispersion and evacuation) on Optical Flow Manifolds (OFM) using Optical Flow Vector (OFV) and Optical Flow Bundles (OFB).…”

Section: Discussionmentioning

confidence: 99%

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

et al. 2020

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Crowd management and monitoring is crucial for maintaining public safety and is an important research topic. Developing a robust crowd monitoring system (CMS) is a challenging task as it involves addressing many key issues such as density variation, irregular distribution of objects, occlusions, pose estimation, etc. Crowd gathering at various places like hospitals, parks, stadiums, airports, cultural and religious points are usually monitored by Close Circuit Television (CCTV) cameras. The drawbacks of CCTV cameras are: limited area coverage, installation problems, movability, high power consumption and constant monitoring by the operators. Therefore, many researchers have turned towards computer vision and machine learning that have overcome these issues by minimizing the need of human involvement. This review is aimed to categorize, analyze as well as provide the latest development and performance evolution in crowd monitoring using different machine learning techniques and methods that are published in journals and conferences over the past five years.

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Section: Discussionmentioning

confidence: 99%

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

et al. 2020

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“…Building Exodus [75] and Simulex [76] are examples of simulation software with rough grid models. Legion [77] and Pathfinder [78] are examples of simulation software with continuous models. The results of evacuation simulation software with fine grid models are well in agreement with the actual situation in detail.…”

Section: Evacuation Modelsmentioning

confidence: 99%

A Scientometric Research on Applications and Advances of Fire Safety Evacuation in Buildings

Yang

Yao

2023

Fire

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Fire safety evacuation has been used in numerous different kinds of buildings. This research conducts a scientometric review of fire safety evacuation applications and advances in the buildings to clarify the research trends of fire evacuation in the future and provide guidance for relevant research. A total of 3312 journals and conference proceedings were analyzed through different dimensions. The result proves that evacuation environments concentrate mainly on residential building, commercial building, school, and railway station. The characteristics of the evacuee have been gradually refined in recent years, including children, the elderly, patients, and vulnerable groups. The main experimental approaches of fire safety evacuation are evacuation drills, site records, and VR/AR experiments. The crowd behavior models mainly consist of six types: a cellular automata model, a social force model, a lattice gas model, a game-theoretic model, an animal agent-based model, and a computer agent-based model. The analysis results in the theoretical method are becoming gradually closer to the behavioral characteristics and movement data of the crowd during the actual evacuation with improvements of practical considerations. The study of evacuation drills, disaster rescue, emergencies, and other external environmental factors will become the forefront of future research, and subway stations, airports, high-rise building, and other personnel places will be the focus of the study of crowd evacuation.

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“…It is based on laser detection method, electronic computer, precision moving carrier attitude detection method, and precision moving [20]. e rapid development of GPS differential positioning technology provides a more powerful scientific and technical guarantee for the acquisition of large-scale urban scene 3D modeling data [21]. It can quickly, in real time, and automatically acquire dense 3D point cloud information in the measurement area and is a new and fast 3D point cloud data acquisition method.…”

Section: Lidar Scanning Point Cloud Acquisitionmentioning

confidence: 99%

A Local Enhancement Method for Large-Scale Building Facade Depth Images using Densely Matched Point Clouds

Guo

Xu³

2022

Advances in Multimedia

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In recent years, laser scanning systems have been widely used to acquire multi-level three-dimensional spatial objects in real time. The laser scanning system is used to acquire the three-dimensional point cloud data of urban scenes. Due to the large-scale characteristics of urban scenes, and the problems of scanning occlusion, scanning path, and limited scanning laser range, the laser scanning system cannot scan every object in the scene comprehensively, multidirectionally and finely, so the corresponding three-dimensional point cloud data collected by many objects are incomplete, and the data images are relatively sparse and unevenly distributed. The existing point cloud denoising and enhancement algorithms, such as AMLS, RMLS, LOP, and WLOP, all use local information to enhance the missing or sparse parts of the point cloud. This point cloud enhancement method is only limited to a small range and cannot do anything for the larger missing area of the point cloud. Even if it is done reluctantly, the effect is not satisfactory. There are a lot of repetitive and similar features in urban buildings, such as the repetitive areas of floors and balconies in buildings. These repetitive areas are distributed in different positions of point clouds, so the repetitive information has non local characteristics. Based on the nonlocal characteristics of building point cloud data and the repetitive structure of buildings, this article proposes a nonlocal point cloud data enhancement algorithm, which organizes the point cloud data in the repeated area into a set of basic geometric elements (planes). The structures are registered in a unified coordinate system, and the point cloud is enhanced and denoised through two denoising processes, “out-of-plane” and “in-plane.”

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Analyzing the Crowd Evacuation Pattern of a Large Densely Populated Building

Cited by 17 publications

References 25 publications

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

A Scientometric Research on Applications and Advances of Fire Safety Evacuation in Buildings

A Local Enhancement Method for Large-Scale Building Facade Depth Images using Densely Matched Point Clouds

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