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Action Recognition Using Hierarchical Independent Subspace Analysis with Trajectory

  • Vinh D. LuongEmail author
  • Lipo Wang
  • Gaoxi Xiao
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 1)

Abstract

Action recognition in videos is an important and challenging problem in computer vision. One of the most crucial aspects of a successful action recognition system is its feature extraction component. Stacked, convolutional Independent Subspace Analysis (SC-ISA), has the best result among unsupervised learning algorithms for action recognition in Hollywood 2 (53.3%) and Youtube (75.8%). However, its performance still lags behind the current state-of-the-art, which uses computer vision-based feature engineering extraction techniques, by about 10%. In this paper, we improve SC-ISA’s results by incorporating motion information into SC-ISA. By extracting blocks following motion trajectories in videos, we are able to reduce noise and increase the number of training samples without degrading the network’s performance when training and testing SC-ISA. We increase SC-ISA’s result by about 1%.

Keywords

Independent Component Analysis Action Recognition Independent Component Analysis Convolutional Neural Network Training Block 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

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