IBaggedFCNet: An Ensemble Framework for Anomaly Detection in Surveillance Videos

The prevalent use of surveillance cameras in public places and advancements in computer vision warrant most sought-after research in the domain of anomalous activity detection. Anomaly detection has shown promising applications for suspicious activity detection. In this paper, we propose a bagging framework IBaggedFCNet that leverages the power of ensembles for robust classification to detect anomalies in videos. Our approach, which investigates state-of-the-art Inception-v3 image classification network, requires no video segmentation prior to feature extraction that can produce unstable segmentation results and cause a high memory footprint. We show improvement empirically on multiple benchmark datasets, most prominently on the UCF-Crime dataset. Moreover, we experiment with different ensemble fusion methods, including static and dynamic techniques, and also prove our single model's predictive accuracy in localizing anomaly in surveillance videos.