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Activity modeling using event probability sequences.

N P Cuntoor1, B Yegnanarayana, R Chellappa

  • 1Signal Innovations Group, Research Triangle Park, NC 27702, USA. nareshpc@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 9, 2008
PubMed
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This study introduces a novel probabilistic method using hidden Markov models (HMMs) to detect events in human motion trajectories for activity recognition. The approach effectively identifies subtle activity changes and anomalies, proving robust across different viewing directions.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human Activity Recognition

Background:

  • Human activity recognition is crucial for surveillance and human-computer interaction.
  • Traditional methods often struggle with subtle changes and variations in viewpoint.
  • Event detection in motion trajectories offers a promising avenue for improved activity analysis.

Purpose of the Study:

  • To develop a probabilistic framework for representing and detecting events in human motion trajectories.
  • To enhance the robustness of activity recognition systems against variations in viewing direction.
  • To enable the detection of subtle changes and anomalies in human activities.

Main Methods:

  • Utilizing hidden Markov models (HMMs) to probabilistically model activities.

Related Experiment Videos

  • Representing activities as sequences of localized events identified by HMMs.
  • Computing event probability sequences for motion trajectories to pinpoint event occurrences.

    • Main Results:

    • Event probability sequences derived from HMMs demonstrate robustness to changes in viewing direction.
    • The proposed event representation facilitates effective activity recognition and anomaly detection.
    • Experiments on diverse datasets (UCF, CMU, TSA) yielded encouraging results.

    Conclusions:

    • The probabilistic event representation using HMMs provides a powerful tool for analyzing human motion.
    • The method's view invariance is a significant advantage for real-world applications.
    • This approach advances the field of human activity recognition and anomaly detection.