IBaggedFCNet: An Ensemble Framework for Anomaly Detection in Surveillance Videos
Yumna Zahid, Muhammad Atif Tahir, Muhammad Durrani, Ahmed Bouridane
Abstract
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.