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Behaviour recognition using multivariate m-mediod based modelling of motion trajectories

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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.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bahria University, Islamabad, 44000, Pakistan

    Shehzad Khalid & Shahid Razzaq

  2. Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Science and Technology, Rawalpindi, 44000, Pakistan

    Usman Akram

Authors
  1. Shehzad Khalid
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  2. Usman Akram
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  3. Shahid Razzaq
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Correspondence to Shehzad Khalid.

Additional information

Communicated by Q. Tian.

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Khalid, S., Akram, U. & Razzaq, S. Behaviour recognition using multivariate m-mediod based modelling of motion trajectories. Multimedia Systems 21, 485–505 (2015). https://doi.org/10.1007/s00530-014-0413-x

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