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Applied Computational Intelligence and Soft Computing
Volume 2017, Article ID 4019213, 7 pages
https://doi.org/10.1155/2017/4019213
Research Article

Representation for Action Recognition Using Trajectory-Based Low-Level Local Feature and Mid-Level Motion Feature

School of Computer Engineering and Sciences, Shanghai University, Shanghai 200444, China

Correspondence should be addressed to Xiaoqiang Li; nc.ude.uhs.i@ilqx

Received 4 August 2016; Revised 22 March 2017; Accepted 17 September 2017; Published 19 October 2017

Academic Editor: Francesco Carlo Morabito

Copyright © 2017 Xiaoqiang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The dense trajectories and low-level local features are widely used in action recognition recently. However, most of these methods ignore the motion part of action which is the key factor to distinguish the different human action. This paper proposes a new two-layer model of representation for action recognition by describing the video with low-level features and mid-level motion part model. Firstly, we encode the compensated flow (-flow) trajectory-based local features with Fisher Vector (FV) to retain the low-level characteristic of motion. Then, the motion parts are extracted by clustering the similar trajectories with spatiotemporal distance between trajectories. Finally the representation for action video is the concatenation of low-level descriptors encoding vector and motion part encoding vector. It is used as input to the LibSVM for action recognition. The experiment results demonstrate the improvements on J-HMDB and YouTube datasets, which obtain 67.4% and 87.6%, respectively.