A Better Trajectory Shape Descriptor for Human Activity Recognition

  • Pejman HabashiEmail author
  • Boubakeur Boufama
  • Imran Shafiq Ahmad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10317)


Sparse representation is one of the most popular methods for human activity recognition. Sparse representation describes a video by a set of independent descriptors. Each of these descriptors usually captures the local information of the video. These features are then mapped to another space, using Fisher Vectors, and an SVM is used for clustering them. One of the sparse representation methods proposed in the literature uses trajectories as features. Trajectories have been shown to be discriminative in many previous works on human activity recognition. In this paper, a more formal definition is given to trajectories and a new more effective trajectory shape descriptor is proposed. We tested the proposed method against our challenging dataset and demonstrated through experiments that our new trajectory descriptor outperforms the previously existing main shape descriptor with a good margin. For example, in one case the obtained results had a 5.58% improvement, compared to the existing trajectory shape descriptor. We run our tests over sparse feature sets, and we are able to reach comparable results to a dense sampling method, with fewer computations.


Human activity recognition Trajectory descriptor Trajectory encoding Shape descriptor Shape encoding 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pejman Habashi
    • 1
    Email author
  • Boubakeur Boufama
    • 1
  • Imran Shafiq Ahmad
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

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