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Learning People Trajectories Using Semi-directional Statistics
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
This paper proposes a system for people trajectory shape analysis by exploiting a statistical approach which accounts for sequences of both directional (the directions of the trajectory) and linear (the speeds) data. A semi-directional distribution (AWLG -Approximated Wrapped and Linear Gaussian) is used with a mixture to find main directions and speeds. A variational version of the mutual information criterion is proposed to prove the statistical dependency of the data. Then, in order todoi:10.1109/avss.2009.34 dblp:conf/avss/CalderaraPC09 fatcat:4xrcy3h4yrdkpeavu6iqal7tdu