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dc.contributor.author | Dr Shehzad Khalid | |
dc.contributor.author | Usman Akram | |
dc.contributor.author | Shahid Razzaq | |
dc.date.accessioned | 2017-11-22T12:24:15Z | |
dc.date.available | 2017-11-22T12:24:15Z | |
dc.date.issued | 2015 | |
dc.identifier.uri | http://hdl.handle.net/123456789/5050 | |
dc.description.abstract | The importance of behaviour analysis and activity recognition systems continue to increase with the increasing demand and deployment of video surveillance systems. Motion trajectories provide rich spatio-temporal information about an object’s activity. In this article, we present a supervised feature extraction and multivariate modelling approach for motion-based behaviour recognition and anomaly detection. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT-based coefficient feature space representation. We employ supervised dimensionality reduction using Local Fisher Discriminant Analysis to enhance the feature space representation of trajectories. A modelling approach, referred to as multivariate m-mediods, is proposed that can cater for the presence of multivariate distribution of samples within a given motion pattern. A hierarchical indexing of mediods and retrieval approach is presented to improve the efficiency of proposed classifier. Our proposed techniques are validated using variety of simulated and complex real-life trajectory datasets. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Bahria University Islamabad Campus | en_US |
dc.relation.ispartofseries | ;DOI 10.1007/s00530-014-0413-x | |
dc.subject | Department of Computer Engineering CE | en_US |
dc.title | Behaviour recognition using multivariate m‑mediod based modelling of motion trajectories | en_US |
dc.type | Article | en_US |