Fusion of Dense SURF Triangulation Features and Dense Trajectory based Features
نویسندگان
چکیده
In this paper, we describe our method used to achieve our results which was submitted to the Recognition Task of the challenge. As for video features, we combined our proposed feature [1] and the dense trajectories based feature presented in [2]. We employed Fisher Vector encoding to represent videos using these features and trained multiclass linear SVMs to perform action recognition. We conducted experiments on UCF-101 dataset following the suggested competition track evaluation setup and obtained precision rate as 66.5%.
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