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

10.48129/kjs.splml.19159

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 is still remains challenging due to the dynamic nature of anomalies. An
active 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 improved anisotropic Gunnar Farneback Optical Flow algorithm. The system is evaluated on the benchmarked
datasets namely UCSD Ped1 UCSD Ped2 and achieves an AUC of 95% and 94% respectively. The experimental
results indicate that the proposed method is superior to state-of-the-art algorithms.