Application of an Incremental SVM Algorithm for On-line Human Recognition from Video Surveillance Using Texture and Color Features

Yanyun Lu (1) , Khaled Boukharouba (1) , Jacques Boonaert (1) , Anthony Fleury (1) , Stéphane Lecoeuche (1)

1 CERI SN - IMT Nord Europe - Centre for Digital Systems

The goal of the paper is to present a new on-line human recognition system, which is able to classify persons with adaptive abilities using an incremental classifier. The proposed incremental SVM is fast, as its training periodrely on only few images and it uses the mathematical properties of SVM to update only the needed parts. In our system, first of all, feature extraction and selection are implemented, based on color and texture features (appearance of the person). Then the incremental SVM classifier has been introduced to recognize a person from a set of 20 persons in CASIA Gait Database. The proposed incremental classifier is updated step by step when a new frame including a person is presented. With this technique, we achieved a correct classification rate of 98.46%, knowing only 5% of the dataset at the beginning of the experiment. A comparison with a non-incremental technique reaches recognition rate of 99% on the same database. Extended analyses have been carried out and showed that the proposed method can be adapted to on-line setting.

Mots clés: Video surveillance; Human recognition; Incremental Support Vector Machine; On-line multiclass classification

Domaines: Apprentissage [cs.LG]

Yanyun Lu, Khaled Boukharouba, Jacques Boonaert, Anthony Fleury, Stéphane Lecoeuche. Application of an Incremental SVM Algorithm for On-line Human Recognition from Video Surveillance Using Texture and Color Features. Neurocomputing, 2014, 126, pp.132-140. ⟨10.1016/j.neucom.2012.08.071⟩. ⟨hal-00988202⟩