Motion Data Mining and Activity Recognition

Name: Motion Data Mining and Activity Recognition
Brand: VDM Verlag Dr. Müller
SKU: 9783639210453
Price: 407.03 RON
Availability: InStock

Motion Classification using Spatio-temporal Approximation of Object Trajectories

Autor: Shehzad Khalid

Limbă:

Legare: Carte broșată

Editura: VDM Verlag Dr. Müller

Disponibilitate: La editor doar la comandă

Expediem în 17-27 zile

407.03 lei

Techniques for understanding video object motion activity are becoming increasingly important with t...

Informații despre carte

Autor

Shehzad Khalid

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2009

Pagini

216

EAN

9783639210453

Enbook ID

06828971

Editura

VDM Verlag Dr. Müller

Dimensiuni

150 x 220 x 13

Descriere completă

Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object s activity. This book presents a novel technique for clustering and classification of object trajectory-based video motion clips using basis function approximation. In the proposed motion learning system, trajectories are treated as time series and modelled using orthogonal basis function representation. A novel framework (Iterative HSACT-SOM) is proposed that exploits the chosen feature subspace and performs efficient and effective motion learning in the presence of significant number of anomalies in training data. A novel modelling technique, referred to as m-Mediods, is proposed that models the class containing n members with m Mediods. Once the m- Mediods based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes using an agglomerative approach.

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Motion Data Mining and Activity Recognition

Motion Classification using Spatio-temporal Approximation of Object Trajectories

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