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Evaluating Feature Importance for Object Classification in Visual Surveillance

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Evaluating Feature Importance for Object Classification in Visual Surveillance
Feature-based object classification, which distinguish a moving object to human or vehicle, is important in visual surveillance. In order to improve classification performance, in addition to choosing between the classification (such as SVM, ANN etc), we have to pay attention to which subset of features to employ in the classifier. This paper describes a method to evaluate the relative importance of various features for object type classification. Starting with a given set of features, we apply the AdaBoost method and then we compute a metric which enables us to choose a good subset of the features. We apply our method to the task of distinguishing whether an image blob is a vehicle, a single human, a human group, or a bike, and we determine that shape-based feature, texture-based feature, and motion-based feature are reliable for this classification task. We validate our method by comparing with performance of ANN-based classification.
Hironobu Fujiyoshi, Masamitsu Tsuchiya
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Hironobu Fujiyoshi, Masamitsu Tsuchiya
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