Crowd Anomaly Detection in Video Frames Using Fine-Tuned AlexNet Model

Khan, Arfat Ahmad; Nauman, Muhammad Asif; Shoaib, Muhammad; Jahangir, Rashid; Alroobaea, Roobaea; Alsafyani, Majed; Binmahfoudh, Ahmed; Wechtaisong, Chitapong

doi:10.3390/electronics11193105

Cited by 25 publications

(14 citation statements)

References 28 publications

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“…Recently, deep learning models represented by convolutional neural networks have developed rapidly. As very effective classification and recognition models, they have attracted considerable attention worldwide, been widely used [11][12][13][14][15], and achieved good results in the agricultural field. Examples include fruit identification [16,17], crop diseases and pests identification [18,19], animal behavior detection [20,21], etc.…”

Section: Introductionmentioning

confidence: 99%

Sugarcane stem node identification algorithm based on improved YOLOv5

Xie,

Li,

Xiao

et al. 2023

PLoS ONE

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Identification of sugarcane stem nodes is generally dependent on high-performance recognition equipment in sugarcane seed pre-cutting machines and inefficient. Accordingly, this study proposes a novel lightweight architecture for the detection of sugarcane stem nodes based on the YOLOv5 framework, named G-YOLOv5s-SS. Firstly, the study removes the CBS and C3 structures at the end of the backbone network to fully utilize shallow-level feature information. This enhances the detection performance of sugarcane stem nodes. Simultaneously, it eliminates the 32 times down-sampled branches in the neck structure and the 20x20 detection heads at the prediction end, reducing model complexity. Secondly, a Ghost lightweight module is introduced to replace the conventional convolution module in the BottleNeck structure, further reducing the model’s complexity. Finally, the study incorporates the SimAM attention mechanism to enhance the extraction of sugarcane stem node features without introducing additional parameters. This improvement aims to enhance recognition accuracy, compensating for any loss in precision due to lightweight modifications. The experimental results showed that the average precision of the improved network for sugarcane stem node identification reached 97.6%, which was 0.6% higher than that of the YOLOv5 baseline network. Meanwhile, a model size of 2.6MB, 1,129,340 parameters, and 7.2G FLOPs, representing respective reductions of 82%, 84%, and 54.4%. Compared with mainstream one-stage target detection algorithms such as YOLOv4-tiny, YOLOv4, YOLOv5n, YOLOv6n, YOLOv6s, YOLOv7-tiny, and YOLOv7, G-YOLOv5s-SS achieved respective average precision improvements of 12.9%, 5.07%, 3.6%, 2.1%, 1.2%, 3%, and 0.4% in sugarcane stem nodes recognition. Meanwhile, the model size was compressed by 88.9%, 98.9%, 33.3%, 72%, 92.9%, 78.8% and 96.3%, respectively. Compared with similar studies, G-YOLOv5s-SS not only enhanced recognition accuracy but also considered model size, demonstrating an overall excellent performance that aligns with the requirements of sugarcane seed pre-cutting machines.

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Section: Introductionmentioning

confidence: 99%

Sugarcane stem node identification algorithm based on improved YOLOv5

Xie,

Li,

Xiao

et al. 2023

PLoS ONE

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“…It focuses on identifying typical occurrences in video streams, employs a Region Proposal Network (RPN), and achieves notable metrics: AUC 93%, ROC 89%, and Accuracy 87%. Focusing on crowd anomaly detection, this paper [18] works with Avenue, UCSD Ped1, and UCSD Ped2 datasets.…”

Section: IVmentioning

confidence: 99%

“…Convolutional AE [32] Frequent false positives, low interpretability 0.6177 RPN [33] Needs adaptation, object-background issues AlexNet [36] Inconsistent results, struggles with complex anomalies 0.9 MOG [37] Quality of input data, Computational complexity 0.969…”

Section: Table I Accuracy and Auc For The Major Reviewed Modelsmentioning

confidence: 99%

“…Challenges persist, including model limitations, dataset diversity, and edge computing complexities. Computer science publications, 20 million+ articles, conferences, books UCSD [12], [13], [18], [19], [21], [24], [28], [34], [36] Anomaly detection, video, 1617 videos, normal/abnormal activities CUHK Avenue [12], [13], [17], [18], [19], [21], [24], [34], [36] Pedestrian re-identification, high-resolution, 31k images, 1.5k identities ShanghaiTech [12], [17], [21], [24], [34] Person re-identification, large-scale, 486 cameras, 306k images, 111k identities UMN [13], [22], [24], [28], [29], [ [25] Traffic flow forecasting, anomaly detection Vishnu Society Data [26] Gated community, India, vehicles, pedestrians, events, images, videos Hockey Fights [29] Video, hockey fights, 11k videos, fight detection, outcome Violent Flows [29] Video, crowd anomaly detection, 126 videos, violent/non-violent events How-ever, through collaborative efforts and diverse methodologies, the surveyed papers are expanding the horizons of what's achievable, enhancing public safety and security. As the field continues to evolve, it is the intersection of these insights that will pave the way for the future of anomaly detection in video surveillance, ensuring a safer world.…”

Section: Table I Accuracy and Auc For The Major Reviewed Modelsmentioning

confidence: 99%

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Anomaly Detection in Crowd Surveillance using Edge Computing: A Comprehensive Survey

Juvekar,

Bhadgaonkar,

Ghadge

et al. 2024

IJRASET

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As urban environments continue to witness the proliferation of surveillance systems, ensuring public safety and security has become an increasingly challenging endeavor. Anomaly detection in crowded areas has emerged as a crucial task in these settings, aimed at identifying suspicious activities or potential threats. To meet the demands of real-time monitoring and analysis, edge computing has gained prominence as a critical technology. This survey paper provides a comprehensive overview of the state-of-the-art in anomaly detection, its use in crowd surviellance and the role played by edge computing in anomaly detection .The paper reviews an extensive body of literature encompassing various techniques and methodologies employed for anomaly detection in crowded scenes. It explores the evolution of traditional video-based approaches, such as motion analysis and object tracking, and the recent advancements leveraging deep learning, including various models in machine learning. These techniques are examined for their applicability in crowd surveillance scenarios.Various commonly used datasets to measure the quality of anomaly detection are also explored, along with their attributes and descriptions.The survey analyzes how edge computing solutions, such as edge AI accelerators and edge devices, enable faster and more context-aware processing of video data, while also addressing issues related to bandwidth constraints, privacy concerns, and scalability; paving way for a further research in harnessing the power of edge computing in crowd surveillance anomaly detection.

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“…Further, Khan, et al [60] used a fine-tuned AlexNet for crowd anomaly detection in video frames. This fine-tuned AlexNet with one less convolutional layer extracts features on three anomaly detection public datasets and inputs them to six classifiers for comparative analysis.…”

Section: Applicationsmentioning

confidence: 99%

Review of AlexNet for Medical Image Classification

Tang,

Sun,

Wang

et al. 2023

EAI Endorsed Trans e-Learn

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In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.

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Crowd Anomaly Detection in Video Frames Using Fine-Tuned AlexNet Model

Cited by 25 publications

References 28 publications

Sugarcane stem node identification algorithm based on improved YOLOv5

Sugarcane stem node identification algorithm based on improved YOLOv5

Anomaly Detection in Crowd Surveillance using Edge Computing: A Comprehensive Survey

Review of AlexNet for Medical Image Classification

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