Ryo Yumiba

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.

A Compensation Method of Motion Features with Regression for Deficient Depth Image

In this paper, we propose a method for compensating for motion features that are outside a given viewing angle by using a regression estimate that is based on a correlation between the motion features from human bodies deficient visually, when recognizing the actions of people whose bodies are only partially within the given view. This compensation is good for use in situations where parts of a persons body are partially protruding outside the edges of the viewing angle, and contributes to enlarging the region coverage for action recognition. The motion features and position of the acting person in a depth image are calculated first in the proposed method. Second, the deficit length protruding outside the view angle is calculated, according to the position of the person. Finally, the motion features from the entire body are estimated using a regression estimate from the motion features by selecting the regression coefficients according to the deficit length. The method for improving the effectiveness of the F-measure is confirmed using three kinds of motion features in a fundamental laboratory experiment. We found from the experimental results that the F-measure was improved by more 12.5% when using motion feature compensation compared to without compensation when the person within the viewing angle cannot actually be seen from the floor to 630 mm above it.