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Situation Graph Trees

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Computer Vision

Synonyms

Generically describable situation; Situation scheme

Definition

Situation Graph Trees (SGT) provide a deterministic formalism to represent the knowledge required for human behavior modeling.

Background

In many domains, high-level descriptions to represent the status of an environment are desirable. Describing a situation requires to conceptualize the knowledge about the possible actions of the actors involved in the environment and their possible interactions. Conceptual descriptions of different application domains like traffic analysis [1], parking lot security [2, 3], and human behavior recognition [4] are of primary importance. The conceptualization can proceed from simple descriptions (simple events) to complex descriptions (complex events). Following relations, concepts can be aggregated into more complex concepts. Hence, an event can be described as a sequence of simple events. To allow such an incremental description of the events, two main processes are required: (a)...

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References

  1. Haag M, Nagel H (2000) Incremental recognition of traffic situations from video image sequences. Image Vis Comput 18(2):137–153

    Article  Google Scholar 

  2. Micheloni C, Remagnino P, Eng HL, Geng J (2010) Intelligent monitoring of complex environments. Intelligent Systems, IEEE 25(3):12–14

    Article  Google Scholar 

  3. Micheloni C, Snidaro L, Foresti G (2009) Exploiting temporal statistics for events analysis and understanding. Image Vis Comput 27(10):1459–1469

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  4. . Arens M, Nagel H (2003) Behavioral knowledge representation for the understanding and creation of video sequences. In: German conference on artificial intelligence, Hamburge(GE), pp 149–163

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  5. Robertson N, Reid I (2006) A general method for human activity recognition in video. Comput Vis Image Underst 104(2–3):232–248

    Article  Google Scholar 

  6. Galata A, Johnson N, Hogg D (2001) Learning variable-length markov models of behavior. Comput Vis Image Underst 81(3):398–413

    Article  MATH  Google Scholar 

  7. Piciarelli C, Micheloni C, Foresti G (2008) Trajectory-based anomalous event detection. IEEE Trans Circuits Syst Video Technol 18(11):1544–1554

    Article  Google Scholar 

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© 2014 Springer Science+Business Media New York

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Micheloni, C., Foresti, G.L. (2014). Situation Graph Trees. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_313

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