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Detection and Recognition of Periodic, Non-Rigid Motion
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@Article{Polana:97,
  author = 	 {Ramprasad Polana, and Randal C. Nelson},
  title = 	 {Detection and Recognition of Periodic, Non-Rigid Motion},
  journal = 	 {International Journal of Computer Vision},
  year = 	 {1997},
 
  volume = 	 {23},
  number = 	 {3},
  pages = 	 {261-282},
  month = 	 {June - July},
  
  abstract =  {  The recognition of nonrigid motion, particularly that arising from human movement (and by
extension from the locomotory activity of animals) has typically made use of high­level parametric
models representing the various body parts (legs, arms, trunk, head etc.) and their connections to
each other. Such model­based recognition has been successful in some cases; however, the methods
are often difficult to apply to real­world scenes, and are severely limited in their generalizability.
The first problem arises from the difficulty of acquiring and tracking the requisite model parts,
usually specific joints such as knees, elbows or ankles. This generally requires some prior high­level
understanding and segmentation of the scene, or initialization by a human operator. The second
problem, with generalization, is due to the fact that the human model is not much good for dogs or
birds, and for each new type of motion, a new model must be hand­crafted. In this paper, we show
that the recognition of human or animal locomotion, and, in fact, any repetitive activity can be
done using low­level, non­parametric representations. Such an approach has the advantage that the
same underlying representation is used for all examples, and no individual tailoring of models or
prior scene understanding is required. We show in particular, that repetitive motion is such a strong
cue, that the moving actor can be segmented, normalized spatially and temporally, and recognized
by matching against a spatio­temporal template of motion features. We have implemented a real­
time system that can recognize and classify repetitive motion activities in normal gray­scale image
sequences. Results on a number of real­world sequences are described. }
}