THIS PAGE INCORPORATES CASCADING STYLE SHEET! TO VIEW PAGE CORRECTLY PLEASE UPDATE TO A CSS COMPLIANT BROWSER
 |
Nonparametric Recognition of Nonrigid Motion |
pdf 716 KB
ps.gz 2.07 MB
ps 6.05 MB
@TechReport{Polana:TR95,
author = {Ramprasad B. Polana and Randal C. Nelson},
title = {Nonparametric Recognition of Nonrigid Motion},
institution = {Comp. Sci Dept University of Rochester},
year = {1995},
number = {575},
address = {Rochester NY},
month = {March},
abstract = {
The recognition of nonrigid motion, particularly that arising from human movement
(and by extension from the locomotory activity of animals) has typically made use
of high-level parametric models representing the various body parts (legs, arms,
trunk, head, etc.) and their connections to each other. Such model-based
recognition has been successful in some cases; however, the methods are often
difficult to apply to real-world scenes, and are severely limited in their
generalizability. The first problem arises from the difficulty of acquiring and
tracking the requisite model parts, usually specific joints such as knees, elbows
or ankles. This generally requires some prior high-level understanding and
segmentation of the scene, or initialization by a human operator. The second
problem is due to the fact that the human model is not much good for dogs or
birds; for each new type of motion, a new model must be hand-crafted. In this
paper, we show that the recognition of human or animal locomotion, and, in fact,
any repetitive activity, can be done using low-level, non-parametric
representations. Such an approach has the advantage that the same underlying
representation is used for all examples, and no individual tailoring of models or
prior scene understanding is required. We show in particular that repetitive
motion is such a strong cue that the moving actor can be segmented, normalized
spatially and temporally, and recognized by matching against a spatio-temporal
template of motion features. We have implemented a real-time system that can
recognize and classify repetitive motion activities in normal gray-scale image
sequences. Results on a number of real-world sequences are described. }
}