Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance; 2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the LongTerm infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches