Kniha Motion Data Mining and Activity Recognition Shehzad Khalid

Motion Data Mining and Activity Recognition

Motion Classification using Spatio-temporal Approximation of Object Trajectories

Jazyk: Angličtina
Vazba: Brožovaná
Dostupnost: U nakladatele na objednávku
Odesíláme za 17-27 dnů
1 875
Techniques for understanding video object motion activity are becoming increasingly important with t...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2009
Stránek
216
EAN
9783639210453
Enbook ID
06828971
Rozměry
150 x 220 x 13

Kompletní popis

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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