Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model

Abstract

Around the world, the video surveillance system has gained wide acceptance and astonishing growth due to its broad applications. The surveillance system has become a paramount tool and benchmark for analyzing the harmony and safety of society. Anomaly detection and its associated applications play a key role in the integrity of the system. The aim of anomaly detection is to find rare and sparse occurrences of events from videos. Developing an accurate and time-efficient system i s s till remains c hallenging due to t he dynamic nature of a nomalies. The deep learning-based end-to-end system with full use of both spatial and temporal features from the input videos is proposed. The model combines the use of 2DCNN and Stacked LSTM to extract frame-level features through an anisotropic Gunnar Farneback Optical Flow algorithm. The system is evaluated on the benchmarked datasets namely UCSD Ped1 and UCSD Ped2, and it achieves an AUC of 95% and 94% respectively. The experimental results indicate that the proposed method is superior to state-of-the-art algorithms.