Euclidean path modeling for video surveillance

12 years 5 months ago
Euclidean path modeling for video surveillance
In this paper, we address the issue of Euclidean path modeling in a single camera for activity monitoring in a multi-camera video surveillance system. The method consists of a path building training phase and a testing phase. During the unsupervised training phase, after auto-calibrating a camera and thereafter metric rectifying the input trajectories, a weighted graph is constructed with trajectories represented by the nodes, and weights determined by a similarity measure. Normalized-cuts are recursively used to partition the graph into prototype paths. Each path, consisting of a partitioned group of trajectories, is represented by a path envelope and an average trajectory. For every prototype path, features such as spatial proximity, motion characteristics, curvature, and absolute world velocity are then recovered directly in the rectified images or by registering to aerial views. During the testing phase, using our simple yet efficient similarity measures for these features, we see...
Imran N. Junejo, Hassan Foroosh
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where IVC
Authors Imran N. Junejo, Hassan Foroosh
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