Anomalous Crowd Event Analysis Using Isometric Mapping

Rao, Aravinda S.; Gubbi, Jayavardhana; Palaniswami, Marimuthu

doi:10.1007/978-3-319-28658-7_35

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…In [44], the authors have presented a framework for crowd behavior using an Enhanced Context-Aware Framework and achieved experimental results with the accuracy of 99.1% and 2.8% of False Negative Rate (FNR) indicating a significant improvement over the 92.0% accuracy and FNR of 31.3% of the Basic Context-Aware Framework (BCF). The detection of anomalous crowd behavior has been monitored in [45] using Isometric Mapping (ISOMAP). During the monitoring and detecting of crowd behavior the ISOMAP has reduced the computational time significantly.…”

Section: Discussionmentioning

confidence: 99%

“…In [68], Cascade Deep AutoEncoder (CDA) and with the combination of multi-frame optical flow information have been proposed for the detection of crowd behavior. Isometric Mapping (ISOMAP) [45], spatio-temporal [46] and spatio-temporal texture [49] models were used to detect the anomalous crowd detection. In [51] Hybrid Random Matrix (HRM) and deep neural network were used for the detection of violent behavior detection.…”

Section: Crowd Behaviormentioning

confidence: 99%

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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%

Section: Crowd Behaviormentioning

confidence: 99%

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

et al. 2020

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“…Similarly, [ 64 ] uses a deep learning method with optical flow for crowd behaviour detection. Some additional methods which use Isometric Mapping [ 65 ], spatio-temporal [ 66 ] and spatio-temporal texture [ 44 ] can also be explored for details.…”

Section: Approachesmentioning

confidence: 99%

Advances and Trends in Real Time Visual Crowd Analysis

Khan

Albattah

Khan

et al. 2020

Sensors

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Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

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Cluster-Based Monitoring and Location Estimation for Crowd Counting

Kızrak

Bolat

2021

Advances in Intelligent Systems and Computing

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Anomalous Crowd Event Analysis Using Isometric Mapping

Cited by 4 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

Advances and Trends in Real Time Visual Crowd Analysis

Cluster-Based Monitoring and Location Estimation for Crowd Counting

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