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Human Action Recognition by Random Features and Hand-Crafted Features: A Comparative Study

  • Haocheng ShenEmail author
  • Jianguo Zhang
  • Hui Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

One popular approach for human action recognition is to extract features from videos as representations, subsequently followed by a classification procedure of the representations. In this paper, we investigate and compare hand-crafted and random feature representation for human action recognition on YouTube dataset. The former is built on 3D HoG/HoF and SIFT descriptors while the latter bases on random projection. Three encoding methods: Bag of Feature(BoF), Sparse Coding(SC) and VLAD are adopted. Spatial temporal pyramid and a two-layer SVM classifier are employed for classification. Our experiments demonstrate that: 1) Sparse Coding is confirmed to outperform Bag of Feature; 2) Using a model of hybrid features incorporating frame-static can significantly improve the overall recognition accuracy; 3) The frame-static features works surprisingly better than motion features only; 4) Compared with the success of hand-crafted feature representation, the random feature representation does not perform well in this dataset.

Keywords

Action recognition Hand-crafted feature Random representation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of ComputingUniversity of DundeeDundeeUK
  2. 2.Department of Computer Science & TechnologyUnited International CollegeZhuhaiChina

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