Masanori Miyoshi

Moving object detection with background model based on spatio-temporal texture

Background subtraction is a common method for detecting moving objects, but it is yet a difficult problem to distinguish moving objects from backgrounds when these backgrounds change significantly. Hence, we propose a method for detecting moving objects with a background model that covers dynamic changes in backgrounds utilizing a spatio-temporal texture named “Space-Time Patch”, which describes motion and appearance, whereas conventional textures describe appearance only. Our experimental results show the proposed method outperforms one conventional method in three scenes: in an outdoor scene where leaves and branches of a tree are waving in intermittent wind, in an indoor scene where ceiling lights are turned on and off frequently, and in an escalator scene beside a window facing outdoors where some passengers are leaning over the hand-rail.

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%.

Integration of Image and ID-POS in ISZOT for Behavior Analysis of Shoppers

Recently the tendency of security oneself is growing because of increasing crimes. So a number of security cameras and the place setting the cameras are increasing, but those cameras are not used effectively. They are usually used for arrest a criminal after crime happens. The research analyzing behavior of shoppers by using a security camera have been studied. There are some methods, using the behavior of part of body, using the trajectories. In this paper, use the trajectories and analyze behavior of shoppers and introduce a method of calculate similarities and clustering them.