Feature Evaluation and Selection for Video Object Classification
Automatic video object classification is the key technology of intelligent video surveillance. In order to improve classification performance,we have to choose appropriate object features. The evaluation for the importance of various features is Absent in common approaches of video object classification. A method to evaluate and select features for video object classification is present. We determine which subset of the features should be chosen in video object classification task by applying the Adaboost method to compute a metric which indicates how well the features contribute to the classification performance. We apply the method to the task of distinguishing whether a video object is a pedestrian,a human group,a vehicle or a bicyclist. Experimental results identify its rationality and validity.