Gait-based action recognition via accelerated minimum incremental coding length classifier

Hung Wei Lin, Min Chun Hu, Ja Ling Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, we present a novel human action recognition approach based on gait energy image (GEI) and minimum incremental coding length (MICL) classifier. GEIs are extracted from video clips and transformed into vectors as input features, and MICL is employed to classify each GEI. We also use multiple cameras to capture GEIs of different views, and the voting strategy is applied after the MICL classification results to improve the overall system performance. Experimental results show that the proposed approach can achieve approximately 95% of accuracy. For practical usage, we also speed up the classification time so that it can be accomplished in a very short time. Moreover, other classification methods are used to classify GEIs and the experimental result shows that MICL is the most suitable classifier for this approach. Besides our recorded action clips, the Weizmann dataset is also used to verify the capability of our approach. The experimental results show that our approach is competitive to other state-of-the-art action recognition methods.

Original languageEnglish
Title of host publicationAdvances in Multimedia Modeling - 18th International Conference, MMM 2012, Proceedings
Pages266-276
Number of pages11
DOIs
Publication statusPublished - 2012
Event18th International Conference on Multimedia Modeling, MMM 2012 - Klagenfurt, Austria
Duration: 2012 Jan 42012 Jan 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7131 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Multimedia Modeling, MMM 2012
Country/TerritoryAustria
CityKlagenfurt
Period12-01-0412-01-06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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