IA-SSLM: Irregularity-Aware Semi-Supervised Deep Learning Model for Analyzing Unusual Events in Crowds

Aljaloud, Abdulaziz; Ullah, Habib

doi:10.1109/access.2021.3081050

Cited by 9 publications

(11 citation statements)

References 29 publications

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“…The proposed fused feature methodology that is applied to crowd features is tested on three different types of datasets: Violent-flow (VF) Dataset [23] and unusual crowd activity (UMN) Dataset [24]. The subsections stated below give a detailed description of these datasets.…”

Section: Dataset Detailsmentioning

confidence: 99%

An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

Murthygowda¹,

Krishnegowda²,

Shashikala³

2023

IJEECS

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<p>The uncontrolled outburst in population has led to crowd gatherings in various public places causing panic and disaster in certain unpleasant and extreme conditions. A study on the analysis of crowd accumulation has been carried out for various reasons that include management of crowd, design of a well-planned public space, the possibility for surveillance at every area and transportation systems. A lot of disasters also occurs due to uncontrollable crowd behaviour and poor crowd management. It could result in loss of property, fatalities or casualties. To avoid this, the conduct of a crowd of people has been studied and analyzed to control their movement and traffic. Hence, in this research work, integrated multi-level feature fusion (IMFF) framework is designed to predict the behaviour; further classification based on the local region is carried out to enhance the prediction. In the case of multi-level feature fusion; first level feature fusion utilizes the motion and appearance; second-level feature fusion utilizes the spatial connection and third-level utilizes the temporal connections. Further, the classification approach is integrated based on the local region is used to enhance the crowd behaviour prediction in terms of accuracy and faster. Moreover, performance evaluation is carried out considering the two distinctive datasets.</p>

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Section: Dataset Detailsmentioning

confidence: 99%

An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

Murthygowda¹,

Krishnegowda²,

Shashikala³

2023

IJEECS

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“…Comparative Analysis using Various Algorithms for Web dataset Figure 3 also represents the accuracy of several models that are compared using different crowd analysis methodologies. Namely these analysis techniques for crowd behavior include Irregularity-Aware Semi-Supervised Deep Learning Model (IASSLM) (11) , Convolutional Neural Networks (CNN) (12) , WideResNet (13) and IMFF model using web datasets. The highest accuracy recorded was with the MFF algorithm (99,36 % -Table 1).…”

Section: Figure 2 Comparative Analysis Using Various Algorithms For W...mentioning

confidence: 99%

“…There are efficient predictions that are performed using the concept of entropy reduction. (5) Another proposed system, based on a deep learning model known as CNN (Convolutional Neural Network), is used to detect crowd irregular behaviour in crowd videos using information based on motion. The architecture used in this system helps to differentiate abnormal from normal behaviour.…”

Section: Introductionmentioning

confidence: 99%

Crowd Behavior Analysis and Prediction using the Feature Fusion Framework

Murthygowda¹,

Krishnegowda²,

Shashikala³

2022

Salud, Ciencia y Tecnología

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The increasing number of people is a major cause of disasters that occur due to overcrowding. The gatherings of crowds in public places are a source of panic, which results in disaster. An analytical study was performed on crowd management. This is highly essential for the design of a well-planned public space, the possibility of surveillance in every area, and transportation systems. The disasters that occur due to uncontrollable crowd behaviour involve loss of property, fatalities, or casualties. To avoid this, the crowd’s behaviour was analysed. A MFF (multi-level feature fusion) framework was designed in this paper to predict behaviour. The first level of multi-level feature fusion employs motion and appearance, the second level employs spatial connections, and the third level employs temporal features. The combination of these characteristics aids in the exploitation of crowd behaviour. Furthermore, MFF was evaluated considering the web dataset, considering accuracy, precision, and recall as parameters. Comparative analysis was carried out with various existing methodologies with an accuracy of above 99 %.

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“…However, it is worth noting that the majority of research works concentrate on the realm of anomaly and outlier detection [8]. According to [9], the identification of different types of anomalous events can be challenging due to diverse patterns and typical incidents within distinct scenes. Anomalies are defined as events that deviate from normal patterns, which may not always result in accuracy or effectiveness [10].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

Abubakar,

OTHMANI,

Sabri

et al. 2023

Preprint

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<p>Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.</p>

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IA-SSLM: Irregularity-Aware Semi-Supervised Deep Learning Model for Analyzing Unusual Events in Crowds

Cited by 9 publications

References 29 publications

An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

An integrated multi-level feature fusion framework for crowd behaviour prediction and analysis

Crowd Behavior Analysis and Prediction using the Feature Fusion Framework

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

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