Abstract
Trajectory features are a powerful new way to describe video data. By leveraging the spatio-temporal range and structure of trajectories, they improve activity recognition performance compared to systems based on fixed local spatio-temporal volumes. This chapter places them in context, compares a sparse, generative model of extended trajectories to a dense, discriminative model of local trajectories, and explores ways to extend the sparse system with new kinds of information.
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Acknowledgements
RM thanks the University of Rochester for support and guidance during his graduate education. AT thanks FQRNT for their financial support. CP thanks Ubisoft, NSERC, Google and the University of Rochester for their financial support.
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Messing, R., Torabi, A., Courville, A., Pal, C. (2013). Evaluating and Extending Trajectory Features for Activity Recognition. In: Farinella, G., Battiato, S., Cipolla, R. (eds) Advanced Topics in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5520-1_4
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DOI: https://doi.org/10.1007/978-1-4471-5520-1_4
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