Fast and reliable human action recognition in video sequences by sequential analysis

Hui Fang, Jeyarajan Thiyagalingam, Nik Bessis, Eran Edirisinghe

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

1 Citation (Scopus)

Abstract

Human action recognition from video sequences is a challenging topic in computer vision research. In recent years, many studies have explored the use of deep learning representations to consistently improve the analysis accuracy. Meanwhile, designing a fast and reliable framework is becoming increasingly important given the exponential growth of video data collected for many purposes (e.g. public security, entertainment, and early medical diagnosis etc.). In order to design a more efficient automatic human action annotation method, the sequential probability ratio test, one of the classical statistical sampling scheme, is adapted to solve a multi-classes hypothesis test problem in our work. With the proposed algorithm, the computational cost is reduced significantly without sacrificing the performance of the underlying system. The experimental results based on the UCF101 data set demonstrated the efficiency of the framework compared to the fixed sampling scheme.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages3973-3977
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 20 Feb 2018
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Conference

Conference24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period17/09/1720/09/17

Keywords

  • Convolutional Neural Networks(CNNs)
  • Efficient video analysis
  • Human action recognition
  • Sequential analysis
  • Sequential probability ratio test(SPRT)

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