Incoherent motion detection using a time-series Gram matrix feature

This paper proposes a new method for incoherent motion recognition from video sequences. We use time-series spatio-temporal intensity gradients within a space-time patch. Using a global space-time patch, we found that the gradient feature allows us to distinguish an incoherent motion from a coherent motion without segmentation. Furthermore the algorithm can run in real time even on an embedded device. In this paper, we verify motion recognition performance for actions which we consider coherent (walk/run) and incoherent (turn/squat/inverse walk). To identify the multiple motion classes, we use linear discriminant analysis and the KNN method. As a result, Our method can distinguish multiple-class motion patterns with a detection rate of about 80%. Also the detection rule of incoherent motions is 100% with a false positive rate of less than 10%.