Surveillance detection of anomalous activities with optimized deep learning technique in crowded scenes

Omobayo Ayokunle Esan (Walter Sisulu University)
Dorcas Oladayo Esan (Walter Sisulu University)
Munienge Mbodila (Walter Sisulu University)
Femi Abiodun Elegbeleye (Walter Sisulu University)
Kesewaa Koranteng (Walter Sisulu University)

Bulletin of Electrical Engineering and Informatics
Vol 12, No 3: June 2023

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.