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

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