首页|A Scalable and Generalized Deep Ensemble Model for Road Anomaly Detection in Surveillance Videos

A Scalable and Generalized Deep Ensemble Model for Road Anomaly Detection in Surveillance Videos

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  • NETL
  • NSTL
  • Tech Science Press
Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack Ensemble Road Anomaly Detection (SERAD). SERAD is used to detect and classify the four most happening road anomalies, such as accidents, car fires, fighting, and snatching, through road surveillance videos with high accuracy. The SERAD adapted three pre-trained Convolutional Neural Networks (CNNs) models, namely VGG19, ResNet50 and InceptionV3. The stacking technique is employed to incorporate these three models, resulting in much-improved accuracy for classifying road abnormalities compared to individual models. Additionally, it presented a custom real-world Road Anomaly Dataset (RAD) comprising a comprehensive collection of road images and videos. The experimental results demonstrate the strength and reliability of the proposed SERAD model, achieving an impressive classification accuracy of 98.7%. The results indicate that the proposed SERAD model outperforms than the individual CNN base models.

Convolutional neural networktransfer learningstack ensemble learningroad anomaly detectiondata augmentation

Sarfaraz Natha、Fareed A. Jokhio、Mehwish Laghari、Mohammad Siraj、Saif A. Alsaif、Usman Ashraf、Asghar Ali

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Department of Information Technology, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah, 67480, Pakistan||Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi, 75000, Pakistan

Department of Information Technology, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah, 67480, Pakistan

Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia

School of Business, Torrens University, Sydney, NSW 2007, Australia

School of Engineering and Information Technology, The University of New South Wales (UNSD), Canberra, ACT 2604, Australia

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2024

10.32604/cmc.2024.057684

Computers, materials & continua

Computers, materials & continua

ISSN：1546-2218
年,卷(期)：2024.81(3Pt.1)
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