Detecting moving objects in video by classifying on...

Details Detecting moving objects in video by classifying on... Detecting moving objects in video by classifying on...

2011-03-01

2011-03-01

Koziol, Stephen R (Department: 2624)

Image analysis

Pattern recognition

Feature extraction

C382S224000, C382S159000

Reexamination Certificate

active

07899253

ABSTRACT:
A method constructs a classifier from training data and detects moving objects in test data using the trained classifier. High-level features are generated from low-level features extracted from training data. The high level features are positive definite matrices on an analytical manifold. A subset of the high-level features is selected, and an intrinsic mean matrix is determined. Each high-level feature is mapped to a feature vector on a tangent space of the analytical manifold using the intrinsic mean matrix. An untrained classifier is trained with the feature vectors to obtain a trained classifier. Test high-level features are similarly generated from test low-level features. The test high-level features are classified using the trained classifier to detect moving objects in the test data.

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Pennec et al., “A Riemannian framework for tensor computing,” In Intl. J. of Computer Vision, vol. 66, pp. 41-66, Jan. 2006.

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