| Conference |
| Type of Publication |
| Shape-based Posture and Gesture Recognition in
Videos |
| Title |
|
|
| Authors |
| Electronic Imaging, pp.
114-124, San José, CA, January 2005 |
| Published in |
| The recognition of human postures and
gestures is considered to be highly relevant semantic information
in videos and surveillance systems. We present a new three-step
approach to classifying the posture or gesture of a person based
on segmentation, classification, and aggregation. A background
image is constructed from succeeding frames using motion
compensation and shapes of people are segmented by comparing the
background image with each frame. We use a modified curvature
scale space (CSS) approach to classify a shape. But a major
drawback to this approach is its poor representation of convex
segments in shapes: Convex objects cannot be represented at all
since there are no inflection points. We have extended the CSS
approach to generate feature points for both the concave and
convex segments of a shape. The key idea is to reflect each
contour pixel and map the original shape to a second one whose
curvature is the reverse: Strong convex segments in the original
shape are mapped to concave segments in the second one and vice
versa. For each shape a CSS image is generated whose feature
points characterize the shape of a person very well. The last
step aggregates the matching results. A transition matrix is
defined that classifies possible transitions between adjacent
frames, e.g. a person who is sitting on a chair in one frame
cannot be walking in the next. A valid transition requires at
least several frames where the posture is classified as
standing-up. We present promising results and compare the
classification rates of postures and gestures for the standard
CSS and our new approach. |
| Abstract |
|
shape analysis
posture and gesture recognition
curvature scale space
|
| Keywords |
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