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A probabilistic logic programming event calculus

  • ANASTASIOS SKARLATIDIS (a1) (a2), ALEXANDER ARTIKIS (a1), JASON FILIPPOU (a1) (a3) and GEORGIOS PALIOURAS (a1)
Abstract
Abstract

We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of an LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.

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J. Allen 1983. Maintaining knowledge about temporal intervals. Communications of the ACM 26, 11, 832843.

A. Artikis , M. Sergot and G. Paliouras 2010. A logic programming approach to activity recognition. In Proceedings of ACM Workshop on Events in Multimedia. ACM, New York, NY, USA, 38.

M. Brand , N. Oliver and A. Pentland 1997. Coupled hidden Markov models for complex action recognition. In Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR). D. Plummer and I. Tonvick , Eds. IEEE Computer Society, Los Alamitos, CA, USA, 994999.

M. L. Ginsberg 1990. Bilattices and modal operators. Journal of Logic and Computation 1, 141.

S. Gong and T. Xiang 2003. Recognition of group activities using dynamic probabilistic networks. In Proceedings of International Conference on Computer Vision. IEEE Computer Society, Los Alamitos, CA, USA, 742749.

A. Hakeem and M. Shah 2007. Learning, detection and representation of multi-agent events in videos. Artificial Intelligence 171, 8–9, 586605.

S. Hongeng and R. Nevatia 2003. Large-scale event detection using semi-Hidden Markov Models. In Proceedings of International Conference on Computer Vision. IEEE Computer Society, Los Alamitos, CA, USA, 14551462.

R. Kowalski and M. Sergot 1986. A logic-based calculus of events. New Generation Computing 4, 1, 6796.

L. Liao , D. Fox and H. Kautz 2007. Hierarchical conditional random fields for GPS-based activity recognition. Robotics Research 28, 487506.

L. Rabiner and B. Juang 1986. An introduction to Hidden Markov Models. ASSP Magazine 3, 1, 416.

M. Richardson and P. Domingos 2006. Markov logic networks. Machine Learning 62, 1–2, 107136.

D. Vail , M. Veloso and J. Lafferty 2007. Conditional random fields for activity recognition. In Proceedings of International Conference on Autonomous Agents and Multiagent Systems (AAMAS). ACM, New York, NY, USA, 18.

L. G. Valiant 1979. The complexity of enumeration and reliability problems. SIAM Journal on Computing 8, 410421.

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Theory and Practice of Logic Programming
  • ISSN: 1471-0684
  • EISSN: 1475-3081
  • URL: /core/journals/theory-and-practice-of-logic-programming
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