Behaviour recognition using multivariate m-mediod based modelling of motion trajectories
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 objects 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.
multivariate m-mediods anomaly detection dimensionality reduction object trajectories classification exact indexing motion recognition