mixed signature: an invariant descriptor for 3d motion trajectory perception and recognition
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2012
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Abstract
Motion trajectory contains plentiful motion information of moving objects, for example, human gestures and robot actions. Motion perception and recognition via trajectory are
useful for characterizing them and a flexible descriptor of motion trajectory plays important
role in motion analysis. However, in the existing tasks, trajectories were mostly used in raw
data and effective descriptor is lacking. In this paper, we present a mixed invariant signature
descriptor with global invariants for motion perception and recognition. The mixed signature
is viewpoint invariant for local and global features. A reliable approximation of the mixed
signature is proposed to reduce the noise in high-order derivatives. We use this descriptor for
motion trajectory description and explore the motion perception with DTW algorithm for
salient motion features. To achieve better accuracy, we modified the CDTW algorithm for
trajectory matching in motion recognition. Furthermore, a controllable weight parameter is
introduced to adjust the global features for tasks in different circumstances. The conducted
experiments validated the proposed method.
| Reference Key |
yang2012mathematicalmixed
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|---|---|
| Authors | ;Jianyu Yang;Y. F. Li;Keyi Wang;Yuan Wu;Giuseppe Altieri;Massimo Scalia |
| Journal | journal of power sources |
| Year | 2012 |
| DOI |
10.1155/2012/613939
|
| URL | |
| Keywords |
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