The Function Space of an Activity. Veeraraghavan, Chellapa, Roy-Chowdhury. CVPR 2006


  1. <Was cited as a paper looking for underlying dimension of activity, although I didnt get that>
  2. Based on time warping
  3. <Basically all the abstract:> “Different instances of the same activity may consist of varying relative speeds
    at which the various actions are executed, in addition to other intra- and inter- person variabilities. Most existing
    algorithms for activity recognition are not very robust to intra- and inter-personal changes of the same activity, and
    are extremely sensitive to warping of the temporal axis due to variations in speed profile. In this paper, we provide a
    systematic approach to learn the nature of such time warps while simultaneously allowing for the variations in descriptors for actions. For each activity we learn an ‘average’ sequence that we denote as the nominal activity trajectory. We also learn a function space of time warpings for each activity separately. The model can be used to learn individualspecific warping patterns so that it may also be used for activity based person identification.”
  4. Classically, approaches attempted to correct for viewpoint, or in skeletal structure, but not so much time
  5. Independent of particular features <but depends on them, naturally>
  6. If doing something like averaging, warping is necessary because the arm, for example can only be in one place and not multiple locations; warping allows for proper interpolation
  7. “The model is composed of a nominal activity trajectory and afunction space capturing the permissible activity specific warping transformations.”
  8. Time warping based on more formal methods than heuristics
  9. “Activity recognition is performed by minimizing the warping error between the nominal activity trajectory and
    the test sequence.”
  10. Most time warping algorithms are based on template matching as opposed to “a model where observed trajectories are viewed as a realization of a stochastic process.”
  11. “Template based recognition algorithms are very effective when the test sequence is one among those in the gallery. But they usually have very poor generalization power. Our algorithm has sufficient generalization power since we explicitly make the function space of an activity convex.”
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