Evaluating And Extending Trajectory Features For Activity Recognition

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.