Unsupervised deep learning system for local anomaly event detection in crowded scenes

Anomaly detection in video surveillance is a significant research subject because of its immense use in real-time applications. These days, open spots like hospitals, traffic areas, airports are monitored by video surveillance cameras. Strange occasions in these recordings have alluded to the anomaly. Unsupervised anomaly detection in the video be endowed with many challenges as there is no exact definition of abnormal events. It varies as for various situations. This paper aims to propose an effective unsupervised deep learning framework for video anomaly detection. Raw image sequences are combined with edge image sequences and given as input to the convolutional auto encoder-ConvLSTM model. Experimental evaluation of the proposed work is performed in three different benchmark datasets such as Avenue, UCSD ped1 and UCSD ped2. The proposed method Hybrid Deep Learning framework for Video Anomaly Detection (HDLVAD) reaches better accuracy compared to existing methods. Investigating video streaming in big data is our further research work.