Anomaly from motion: Unsupervised extraction of visual irregularity via motion prediction

Abstract

The problem of automatically extracting anomalous events from any given video is a problem that has been researched from the early days of computer vision. It has still not been fully solved, showing that it is indeed not a trivial problem. The various challenges involved are lack of proper definition, varying scene structure and objects of interest in the scene, just a few to name. In this paper we propose a novel method to extract outliers from motion alone. We employ a stacked LSTM encoder-decoder structure to model the regular motion patterns of the given video sequence. The discrepancy between the motion predicted using the model and the actual observed motion in the scene is analyzed to detect anomalous activities. We perform extensive experimentation on the benchmark datasets of crowd anomaly analysis. We report State of the Art results across all the datasets.