Enhancing Crime Classification in Surveillance Videos for Real Time Monitoring

Abstract

Anomalies can be detected with the help of patterns and events that differ from the normal flow of events. The paradigms of surveillance may relate to abuse, fights, road accidents and snatchings, etc. In real-world surveillance, finding unusual events in these massive video streams is a difficult endeavor, since they often occur inconsistently. However, deep learning-based anomaly detection helps reduce human labor and its decision-making ability can be compared to that of humans, thus ensuring the safety of the public. In the majority of reported studies, anomalies are detected from surveillance videos based on binary classification. The reported approaches did not cover other anomalous events from surveillance videos, including abuse, fights, vehicle accidents, shootings, stealing, vandalism, and robberies. This paper proposes an intelligent anomaly detection framework based on deep features that can operate more efficiently in surveillance networks. In the proposed framework, spatial-temporal features are first extracted from a series of frames by passing them through a CNN model that has been pretrained. Analyzing the frames in a sequence can be beneficial in detecting anomalous events. Once the deep features have been extracted, the data is then passed to the Long Short-Term Memory (LSTM) model. The model can accurately classify ongoing anomalies/normal events in complex surveillance scenes of smart cities. A dataset from the University of Central Florida (UCF) Crime video dataset is used to perform extensive experiments on anomaly detection. We report an increase in data accuracy of 47.83% over state-of-the-art methods for UCF-Crime datasets.