Title: An improved geometric descriptor associated with wavelet transform for aggressive human behaviour recognition
Authors: A. Ouanane; A. Serir
Addresses: Laboratory of Image Processing and Radiation, Université des Sciences et de la Technologie Houari Boumediene (USTHB), BP 32 El Alia, 16111 Bab Ezzouar, Algiers, Algeria ' Laboratory of Image Processing and Radiation, Université des Sciences et de la Technologie Houari Boumediene (USTHB), BP 32 El Alia, 16111 Bab Ezzouar, Algiers, Algeria
Abstract: Actually, the automatic recognition of aggressive human behaviours becomes an important issue to improve the intelligent security systems and enhance the public safety. In this paper, we propose a robust algorithm which aims to detect an aggressive human behaviour from a monocular vision. The proposed algorithm is based on a spatio-temporal descriptor by using a geometric approach. The latter describes the spatial information of the specific limbs of the body such as arm limb in the form of signatures. To extract the spatio-temporal features, the signatures are normalised and then analysed by the one-dimensional discrete wavelet transform. Thus, a binary support vector machine classifier is used in order to separate either aggressive or non-aggressive behaviours. Several tests have been conducted on the KTH dataset. The obtained results show that the proposed method enables robust recognition in challenging situations, and it is suitable in online action recognition with significant accuracy rate.
Keywords: aggressive behaviour; human behaviour; geometric descriptor; scalar quantisation; wavelet transform; SVM classifier; support vector machines; behaviour recognition; intelligent security; public safety; monocular vision; online action recognition.
International Journal of Computational Vision and Robotics, 2014 Vol.4 No.3, pp.171 - 194
Available online: 26 Jun 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article