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All Journal Bulletin of Electrical Engineering and Informatics
Omobayo Ayokunle Esan
Walter Sisulu University
Electrical & Electronics Engineering

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Surveillance detection of anomalous activities with optimized deep learning technique in crowded scenes Omobayo Ayokunle Esan; Dorcas Oladayo Esan; Munienge Mbodila; Femi Abiodun Elegbeleye; Kesewaa Koranteng
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4471

Abstract

The performance of conventional surveillance systems is challenged by high error detection rates in busy scenes, which has significantly affected the accurate detection of the current surveillance system. Feature representation and object pattern extraction from different scenes have made deep learning (DL) promising methods in surveillance systems, compared to the approaches where features are created manually. To improve the detection accuracy, this paper presents an intelligent DL technique that combines convolutional neural network (CNN) and long short-term memory (LSTM). CNN extracts and learns the object features from a set of raw images, while the LSTM is then used by gated mechanisms to store important information from the extracted features. The proposed method was validated using datasets from the University of California San Diego (UCSD). The result shows that the model achieves 95% accuracy, which is superior compared to other conventional detection models.
Co-Authors Dorcas Oladayo Esan Femi Abiodun Elegbeleye Kesewaa Koranteng Munienge Mbodila