Directional Dense-Trajectory-based Patterns for Dynamic Texture Recognition

International audienceRepresentation of dynamic textures (DTs), well-known as a sequence of moving textures, is a challenging problem in video analysis due to disorientation of motion features. Analyzing DTs to make them "under-standable" plays an important role in different applications of computer vision. In this paper, an efficient approach for DT description is proposed by addressing the following novel concepts. First, 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, we 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 our proposal

Tracking objects with co‐occurrence matrix and particle filter in infrared video sequences

Tracking objects in infrared video sequences became a very important challenge for many current tracking algorithms due to several complex situations such as illumination variation, night vision, and occlusion. This study proposes a new tracker that uses a set of invariant parameters calculated via the co‐occurrence moments to better describe the target object. The usage of the co‐occurrence moments gives the ability to exploit the information about the texture of the target to enhance the robustness of the tracking task. This latter is performed without any learning or clustering phase. The qualitative and quantitative studies on challenging sequences demonstrate that the results obtained by the proposed algorithm are very competitive in comparison to several state‐of‐the‐art methods