Kernel null-space-based abnormal event detection using hybrid motion information

Abstract

Abnormal event detection in crowded scenes is a challenging task in the computer vision community. A hybrid motion descriptor named the multiscale histogram of first- and second-order motion is proposed for abnormal event detection. The second-order motion describes the change in motion and is extracted by optical flow-based instantaneous tracking, which avoids object tracking in crowded scenes. For the modeling of normal events, a kernel null Foley–Sammon transform (KNFST) is introduced. KNFST makes a projection in the null space, where normal samples of all types are treated jointly instead of considering each known class individually. Experiments conducted on two benchmark datasets and comparisons to state-of-the-art methods demonstrate the superiority of the proposed method.