Learning neighborhood cooccurrence statistics of sparse features for human activity recognition

@article{Banerjee2011LearningNC,
  title={Learning neighborhood cooccurrence statistics of sparse features for human activity recognition},
  author={Prithviraj Banerjee and Ramakant Nevatia},
  journal={2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
  year={2011},
  pages={212-217}
}
A common approach to activity recognition has been the use of histogram of codewords computed from Spatio Temporal Interest Points (STIPs). Recent methods have focused on leveraging the spatio-temporal neighborhood structure of the features, but they are generally restricted to aggregate statistics over the entire video volume, and ignore local pairwise relationships. Our goal is to capture these relations in terms of pairwise cooccurrence statistics of codewords. We show a reduction of such… CONTINUE READING

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