Video Segmentation Framework Based on Multi-kernel Representations and Feature Relevance Analysis for Object Classification
A video segmentation framework to automatically detect moving objects in a scene using static cameras is proposed. Using Multiple Kernel Representations, we aim to enhance the data separability into the scene by incorporating multiple information sources into the process, and employing a relevance analysis each source is automatically weighted. A tuned Kmeans technique is employed to group pixels as static or moving objects. Moreover, the proposed methodology is tested for the classification of people and abandoned objects. Attained results over real-world datasets, show how our approach is stable using the same parameters for all experiments.
KeywordsBackground subtraction Multiple kernel learning Relevance analysis Data separability
This research was carried out under grants provided by a M.Sc. and a Ph.D. scholarship provided by Universidad Nacional de Colombia, and the project 15,795, funded by Universidad Nacional de Colombia.
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