MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

This paper presents a method for classifying the direction　of movement and for segmenting objects simultaneously using features　of space-time patches. Our approach uses vector quantization to classify　the direction of movement of an object and to estimate its centroid by　referring to a codebook of the space-time patch feature, which is generated
from multiple learning samples. We segmented the objects’ regions　based on the probability calculated from the mask images of the learning　samples by using the estimated centroid of the object. Even though　occlusions occur when multiple objects overlap in different directions of　movement, our method detects objects individually because their direction　of movement is classified. Experimental results show that object　detection is more accurate with our method than with the conventional　method, which is only based on appearance features.