KPCA Spatio-Temporal Trajectory Point Cloud Classifier for Recognizing Human Actions in a CBVR System

Provided by: University of Vienna
Topic: Cloud
Format: PDF
The authors describe a Content Based Video Retrieval (CBVR) software system for identifying specific locations of a human action within a full length film, and retrieving similar video shots from a query. For this, they introduce the concept of a trajectory point cloud for classifying unique actions, encoded in a spatio-temporal covariant eigenspace, where each point is characterized by its spatial location, local Frenet-Serret vector basis, time averaged curvature and torsion and the mean osculating hyperplane. Since each action can be distinguished by their unique trajectories within this space, the trajectory point cloud is used to define an adaptive distance metric for classifying queries against stored actions.

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