Action Recognition using Discriminative Structured Trajectory Groups

"Action Recognition using Discriminative Structured Trajectory Groups"

Indriyati Atmosukarto, Narendra Ahuja, Bernard Ghanem
"Action Recognition using Discriminative Structured Trajectory Groups"
Winter Conference on Applications of Computer Vision (WACV 2015)​​​
Indriyati Atmosukarto, Narendra Ahuja, Bernard Ghanem
Action Recognition
​In this paper, we develop a novel framework for action recognition in videos. The framework is based on automatically learning the discriminative trajectory groups that are relevant to an action. Different from previous approaches, our method does not require complex computation
for graph matching or complex latent models to localize the parts. We model a video as a structured bag of trajectory groups with latent class variables. We model action recognition problem in a weakly supervised setting and learn discriminative trajectory groups by employing multiple instance learning (MIL) based Support Vector Machine (SVM) using pre-computed kernels. The kernels depend on the spatio-temporal relationship between the extracted trajectory groups and their associated features. We demonstrate both quantitatively and qualitatively that the classifi- cation performance of our proposed method is superior to baselines and several state-of-the-art approaches on three challenging standard benchmark datasets.