Galasso, F and Iwasaki, M and Nobori, K and Cipolla, R (2011) Spatio-temporal clustering of probabilistic region trajectories. Proceedings of the IEEE International Conference on Computer Vision. pp. 1738-1745.Full text not available from this repository.
We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences. © 2011 IEEE.
|Divisions:||Div F > Machine Intelligence|
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|Date Deposited:||18 May 2016 18:56|
|Last Modified:||29 Jul 2016 22:12|