Panic Detection in Crowded Scenes

Altamimi, Ahmed B.; Ullah, Habib

doi:10.48084/etasr.3347

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…Crowd modeling involves the application of various simulation tools, as extensively detailed in this survey [10]. In contrast, crowd monitoring primarily relies on computer vision methods, as outlined in [3,11]. Crowd modeling and crowd monitoring research share a common overarching objective, which is to enhance crowd management.…”

Section: B Crowd Managementmentioning

confidence: 99%

The Innovative Role of Process Mining in building Face Re-identification Trajectory

Alharbi

2024

Eng. Technol. Appl. Sci. Res.

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Face recognition and tracking technology have witnessed significant advancements during the recent decades. These advancements include improved accuracy and speed in identifying and tracking individuals, as well as the ability to recognize faces in various scale and lighting conditions. Leveraging the potential of face recognition and tracking, this article explores the integration of process mining techniques to discover and visualize face’s appearing trajectories in crowd scenarios, aiming to enhance crowd security, surveillance, and personal identification. Notably, existing face recognition tools typically focus on bounding box localization, neglecting the utilization of face coordinates to construct trajectory models upon face re-identification. In this paper, full system architecture for building a trajectory model of re-identified faces in a crowd is proposed. This approach significantly helped in building a large database of visitor faces, and the proposed trajectory model resulted in a high rate of true positive face re-identification.

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Section: B Crowd Managementmentioning

confidence: 99%

The Innovative Role of Process Mining in building Face Re-identification Trajectory

Alharbi

2024

Eng. Technol. Appl. Sci. Res.

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“…We conducted a literature review focused on the most recent studies published after 2018, in relation to our research area. One of our primary findings is that image analysis studies have been carried out in various areas of interest on a subject-by-subject basis but have not been conducted to offer references for many general fields [11][12][13]. Some studies have emphasized the need for real-time image processing [14].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Approach to Improve the Video Anomaly Detection Performance of Pixel- and Frame-Based Techniques Using Machine Learning Algorithms

Tutar,

Güneş,

Zontul

et al. 2024

Computation

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With the rapid development in technology in recent years, the use of cameras and the production of video and image data have similarly increased. Therefore, there is a great need to develop and improve video surveillance techniques to their maximum extent, particularly in terms of their speed, performance, and resource utilization. It is challenging to accurately detect anomalies and increase the performance by minimizing false positives, especially in crowded and dynamic areas. Therefore, this study proposes a hybrid video anomaly detection model combining multiple machine learning algorithms with pixel-based video anomaly detection (PBVAD) and frame-based video anomaly detection (FBVAD) models. In the PBVAD model, the motion influence map (MIM) algorithm based on spatio–temporal (ST) factors is used, while in the FBVAD model, the k-nearest neighbors (kNN) and support vector machine (SVM) machine learning algorithms are used in a hybrid manner. An important result of our study is the high-performance anomaly detection achieved using the proposed hybrid algorithms on the UCF-Crime data set, which contains 128 h of original real-world video data and has not been extensively studied before. The AUC performance metrics obtained using our FBVAD-kNN algorithm in experiments were averaged to 98.0%. Meanwhile, the success rates obtained using our PBVAD-MIM algorithm in the experiments were averaged to 80.7%. Our study contributes significantly to the prevention of possible harm by detecting anomalies in video data in a near real-time manner.

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“…The second method optimized storage by using neighboring frame differences and a fixed threshold. Fixed threshold-based motion detection will not work well on variable luminance conditions [14][15]. This paper proposes a novel method for optimizing motion detection and video storage without using external sensors.…”

Section: Imentioning

confidence: 99%

Storage Optimization using Adaptive Thresholding Motion Detection

Atif

Khand

Khan

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Data storage is always an issue, especially for video data from CCTV cameras that require huge amounts of storage. Moreover, monitoring past events is a laborious task. This paper proposes a motion detection method that requires fewer calculations and reduces the required data storage up to 70%, as it stores only the informative frames, enabling the security personnel to retrieve the required information more quickly. The proposed method utilized a histogram-based adaptive threshold for motion detection, and therefore it can work in variable luminance conditions. The proposed method can be applied to streamed frames of any CCTV camera to efficiently store and retrieve informative frames.

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Panic Detection in Crowded Scenes

Cited by 5 publications

References 48 publications

The Innovative Role of Process Mining in building Face Re-identification Trajectory

The Innovative Role of Process Mining in building Face Re-identification Trajectory

A Hybrid Approach to Improve the Video Anomaly Detection Performance of Pixel- and Frame-Based Techniques Using Machine Learning Algorithms

Storage Optimization using Adaptive Thresholding Motion Detection

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