Directional Dense-Trajectory-based Patterns for Dynamic Texture Recognition

Thanh Tuan Nguyen 1 Thanh Phuong Nguyen 1 Frédéric Bouchara 1
1 SIIM - Signal et Image
LIS - Laboratoire d'Informatique et Systèmes
Abstract : Representation 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.
Complete list of metadatas

Cited literature [67 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02470418
Contributor : Thanh Phuong Nguyen <>
Submitted on : Friday, February 7, 2020 - 11:42:24 AM
Last modification on : Tuesday, February 11, 2020 - 1:42:22 AM

File

IET_2019_revised_notCR.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02470418, version 1

Collections

Citation

Thanh Tuan Nguyen, Thanh Phuong Nguyen, Frédéric Bouchara. Directional Dense-Trajectory-based Patterns for Dynamic Texture Recognition. IET Computer Vision, IET, In press. ⟨hal-02470418⟩

Share

Metrics

Record views

10

Files downloads

10