On-Line Human Recognition from Video Surveillance Using Incremental SVM on Texture and Color Features
The goal of this paper is to contribute to the realization of a system able to recognize people in video surveillance images. The context of this study is to classify a new frame including a person into a set of already known people, using an incremental classifier. To reach this goal, we first present the feature extraction and selection that have been made on appearance based on features (from color and texture), and then we introduce the incremental classifier used to differentiate people from a set of 20 persons. This incremental classifier is then updated at each new frame with the new knowledge that has been presented. With this technique, we achieved 92% of correct classification on the used database. These results are then compared to the 99% of correct classification in the case of a nonincremental technique and these results are explained. Some future works will try to rise the performances of incremental learning the one of non-incremental ones.
KeywordsSupport Vector Machine Feature Selection Recognition Rate Classical Support Vector Machine Video Surveillance
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