Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection

Almazroey, Alaa Atallah; Jarraya, Salma Kammoun

doi:10.1007/978-3-030-44289-7_25

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Similarly, Hu [30] employed a combination of the histogram of gradient and CNN for feature extraction, while a least-squares support vector was used for classification. Almazroey and Jarraya [31] focused on utilizing the Lucas-Kanade optical flow method, pre-trained CNN, and feature selection method (neighborhood component analysis) to extract relevant features. They then used a support vector machine to generate a trained classifier.…”

Section: Related Workmentioning

confidence: 99%

A Cloud-Based Deep Learning Framework for Early Detection of Pushing at Crowded Event Entrances

Alia

Maree

Chraibi³

et al. 2023

IEEE Access

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Crowding at the entrances of large events may lead to critical and life-threatening situations, particularly when people start pushing each other to reach the event faster. Automatic and timely identification of pushing behavior would help organizers and security forces to intervene early and mitigate dangerous situations. In this paper, we propose a cloud-based deep learning framework for automatic early detection of pushing in crowded event entrances. The proposed framework initially modifies and trains the EfficientNetV2B0 Convolutional Neural Network model. Subsequently, it integrates the adapted model with an accurate and fast pre-trained deep optical flow model with the color wheel method to analyze video streams and identify pushing patches in real-time. Moreover, the framework uses live capturing technology and a cloud-based environment to collect video streams of crowds in real-time and provide early-stage results. A novel dataset is generated based on five real-world experiments and their associated ground truth data to train the adapted EfficientNetV2B0 model. The experimental setups simulated a crowded event entrance, while the ground truths for each video experiment were generated manually by social psychologists. Several experiments on the videos and the generated dataset are carried out to evaluate the accuracy and annotation delay time of the proposed framework. The experimental results show that the proposed framework identified pushing behaviors with an accuracy rate of 87% within a reasonable delay time.

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Section: Related Workmentioning

confidence: 99%

A Cloud-Based Deep Learning Framework for Early Detection of Pushing at Crowded Event Entrances

Alia

Maree

Chraibi³

et al. 2023

IEEE Access

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“…We also show the websites of the data source. Some more related new datasets that published in recent years are shown in [6,33,36,105].…”

Section: Datasetsmentioning

confidence: 99%

Abnormal Human Behavior Detection in Videos: A Review

Sun

Yuan

et al. 2021

ITC

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Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

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