ENSEMBLE LEARNING USING BAGGING AND INCEPTION-V3 FOR ANOMALY DETECTION IN SURVEILLANCE VIDEOS

The prevalent use of surveillance cameras in public places and advancements in computer vision warrants most sought-after research in the domain of anomalous activity detection. Several approaches have been proposed for the detection of an anomaly in videos. Spatio-temporal features using 3D Convolutional Network (C3D) is a state-of-the-art approach for this problem where deep multiple instance ranking framework is being investigated. However, this approach requires the segmentation of videos before feature extraction that can produce unstable segmentation results and can have a large memory footprint. In this paper, we extract video features using the Inception-v3 deep learning network which eliminates segmentation. Moreover, to improve the robustness of the backbone classifier we propose to use a homogeneous bagging ensemble of the 3-Layer Fully Connected (FC) Network. Experiments are conducted on the UCF-Anomaly detection dataset and exhibit improved performance over existing approaches.