During the last years, the task of automatic event analysis in video sequences has gained an increasing attention among the research community. In surveillance systems anomaly behavior is a very important task. Actually, this task is performed by humans. Automatizing this task is pretty complicated since exist infinity possible situations if we think in a scalable automatic surveillance. To reduce the complexity we may look for patterns, normal and anomalous. An anomalous event might be characterized as an event that deviates from the normal or usual, but not necessarily in an undesirable manner, e.g., an anomalous event might just be different from normal but not a suspicious event from the surveillance stand point.
The current project is about anomalous pattern recognition. Assuming images captured from a single camera, our model use our proposed feature called Flow Orientation and Magnitude (HOFM). This feature is based on optical flow information to describe the normal patterns on the scene, so that we can employ a simple nearest neighbor search to identify whether a given unknown pattern should be classified as an anomalous event. Our descriptor captures spatiotemporal information from cuboids (regions with spatial and temporal support) and encodes both magnitude and orientation of the optical flow separately into histograms.
|1.||(2018): Novel Anomalous Event Detection based on Human-object Interactions. In: VISAPP 2018 - International Conference on Computer Vision Theory and Applications, pp. 1-8, 2018.|
|2.||(2017): Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos. In: IEEE Transactions on Circuits and Systems for Video Technology, 27 (3), pp. 673-682, 2017.|
|3.||(2015): Histograms of Optical Flow Orientation and Magnitude to Detect Anomalous Events in Videos. In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-8, 2015.|