Directional dense‐trajectory‐based patterns for dynamic texture recognition. Issue 4 (15th April 2020)

• Record Type: Journal Article
• Authors: Nguyen, Thanh Tuan; Nguyen, Thanh Phuong; Bouchara, Frédéric
• Is Part Of: IET computer vision. Volume 14:Issue 4(2020)
• Page Start: 162
• Page End: 176
• Publication Date: 2020-04-15
• Subjects: image recognition -- image texture -- image motion analysis -- image representation -- computer vision -- video signal processing -- image sequences; dense trajectories -- spatio-temporal features -- motion points -- dense-trajectory-based descriptors -- DT recognition -- directional dense-trajectory-based patterns -- dynamic texture recognition -- moving textures -- video analysis -- motion features -- computer vision -- DT description -- beneficial properties -- substantial extensions -- local vector pattern operator -- directional features -- directional beams -- directional dense trajectory patterns; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2019.0455
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Representation of dynamic textures (DTs), well‐known as a sequence of moving textures, is a challenging problem in video analysis due to the disorientation of motion features. Analysing DTs to make them 'understandable' plays an important role in different applications of computer vision. In this study, an efficient approach for DT description is proposed by addressing the following novel concepts. First, the beneficial properties of dense trajectories are exploited for the first time to efficiently describe DTs instead of the whole video. Second, two substantial extensions of local vector pattern operator are introduced to form a completed model which is based on complemented components to enhance its performance in encoding directional features of motion points in a trajectory. Finally, the authors present a new framework, called directional dense trajectory patterns, which takes advantage of directional beams of dense trajectories along with spatio‐temporal features of their motion points in order to construct dense‐trajectory‐based descriptors with more robustness. Evaluations of DT recognition on different benchmark datasets (i.e. UCLA, DynTex, and DynTex++) have verified the interest of the authors' proposal.