Recognizing Complex Human Activities via Crowd Context

  • Wongun Choi
  • Silvio Savarese
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 6)


This chapter examines the problem of classifying collective human activities from video sequences. A collective activity is defined by the existence of the coherent behavior among individuals in a spatial and temporal neighborhood. Examples of collective activities are queuing in a line or talking. Such types of activities cannot be just defined by considering actions of individuals in isolation but rather by observing the interactions of nearby individuals in time and space. In this chapter we discuss recent methods for analyzing collective activities through the concept of crowd context. We present various solutions for modeling the crowd context and demonstrate the flexibility and scalability of the proposed framework in a number of experiments on publicly available datasets of collective human activities.


Collective Activity Video Sequence Support Vector Machine Classifier Time Stamp Markov Random Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Electrical and Computer EngineeringUniversity of MichiganAnn ArborUSA

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