Local Abnormality Detection in Video Using Subspace Learning

On-line abnormality detection in video without the use of object detection and tracking is a desirable task in surveillance.We address this problem for the case when labeled information about normal events is limited and information about abnormal events is not available. We formulate this problem as a one-class classification, where multiple local novelty classifiers (detectors) are used to first learn normal actions based on motion information and then to detect abnormal instances. Each detector is associated to a small region of interest and is trained over labeled samples projected on an appropriate subspace. We discover this subspace by using both labeled and unlabeled segments.We investigate the use of subspace learning and compare two methodologies based on linear (Principal Components Analysis) and on non-linear subspace learning (Locality Preserving Projections), respectively. Experimental results on a real underground station dataset shows that the linear approach is better suited for cases where the subspace learning is restricted to the labeled samples, whereas the non-linear approach is preferable in the presence of additional unlabeled data.