Title :
Discrimination of motion based on traces in the space of probability functions over feature relations
Author :
Sarkar, Sudeep ; Vega, Isidro Robledo
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA
Abstract :
In this paper we demonstrate that it is possible to discriminate between high level motion types such as walking, jogging, or running based on just the change in the relational statistics among the detected image features, without the need for object models, perfect segmentation, or tracking. Instead of the statistics of the feature attributes themselves, we consider the distribution of the statistics of the relations among the features. We represent the observed distribution of feature relations in an image as a point in a space where the Euclidean distance is related to the Bhattacharya distance between probability functions. Different motion types sweep out different traces in this Space of Probability Functions (SoPF). We demonstrate the effectiveness of this representation on image sequences of human in motion, gathered using a digital video camera. We show that it is not only possible to distinguish between motion types but also to discriminate between persons based on the SoPF traces.
Keywords :
computational geometry; eigenvalues and eigenfunctions; image sequences; motion estimation; Euclidean distance; digital video camera; feature relations; image sequences; motion discrimination; probability functions; relational statistics; Computer vision; Euclidean distance; Image segmentation; Image sequences; Legged locomotion; Motion detection; Object detection; Probability; Statistical distributions; Tracking;
Conference_Titel :
Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on
Print_ISBN :
0-7695-1272-0
DOI :
10.1109/CVPR.2001.990636