EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos

Sareer Ul Amin, Mohib Ullah, Muhammad Sajjad, Faouzi Alaya Cheikh, Mohammad Hijji, Abdulrahman Hijji and Khan Muhammad
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Sareer Ul Amin: Digital Image Processing Laboratory, Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan
Mohib Ullah: Software, Data and Digital Ecosystems, Department of Computer Science, Norwegian University for Science and Technology (NTNU), 2815 Gjøvik, Norway
Muhammad Sajjad: Digital Image Processing Laboratory, Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan
Faouzi Alaya Cheikh: Software, Data and Digital Ecosystems, Department of Computer Science, Norwegian University for Science and Technology (NTNU), 2815 Gjøvik, Norway
Mohammad Hijji: Industrial Innovation and Robotic Center (IIRC), University of Tabuk, Tabuk 47711, Saudi Arabia
Abdulrahman Hijji: Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dharan 31261, Saudi Arabia
Khan Muhammad: Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea

Mathematics, 2022, vol. 10, issue 9, 1-15

Abstract: Surveillance systems regularly create massive video data in the modern technological era, making their analysis challenging for security specialists. Finding anomalous activities manually in these enormous video recordings is a tedious task, as they infrequently occur in the real world. We proposed a minimal complex deep learning-based model named EADN for anomaly detection that can operate in a surveillance system. At the model’s input, the video is segmented into salient shots using a shot boundary detection algorithm. Next, the selected sequence of frames is given to a Convolutional Neural Network (CNN) that consists of time-distributed 2D layers for extracting salient spatiotemporal features. The extracted features are enriched with valuable information that is very helpful in capturing abnormal events. Lastly, Long Short-Term Memory (LSTM) cells are employed to learn spatiotemporal features from a sequence of frames per sample of each abnormal event for anomaly detection. Comprehensive experiments are performed on benchmark datasets. Additionally, the quantitative results are compared with state-of-the-art methods, and a substantial improvement is achieved, showing our model’s effectiveness.

Keywords: anomaly detection; shots segmentation; computer vision; deep learning; histogram difference; keyframe extraction; intelligent surveillance networks; crime detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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