Vision-based hand grasping posture recognition in drinking activity

Jia Luen Chua, Yoong Choon Chang, Mohamed Hisham Jaward, Jussi Parkkinen, Kok Sheik Wong

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

13 Citations (Scopus)

Abstract

Drinking activity recognition is not a well-researched area in the human activity recognition area. In this paper, a novel technique to recognize the hand grasping posture in drinking activities is proposed. The proposed method aims to overcome the accuracy issue of Kinect in detecting the correct hand position during drinking activities and no training is required to recognize the grasping posture. Instead, the proposed technique directly extracts the unique features of the grasp posture by using a special Haar-like feature on the input image. By comparing the difference between the total pixel values of each region to a set of thresholds, the grasping posture of the hand can be detected and distinguished from other non-grasping postures or non-hand images. Experimental results indicate that the proposed technique is able to achieve a relatively high accuracy (88% true positive rate and 20% false positive rate) in detecting and recognizing the normal hand grasping posture, which mainly appears in drinking activities where someone is holding a cup.

Original languageEnglish
Title of host publication2014 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2014
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages185-190
Number of pages6
ISBN (Electronic)9781479961207
DOIs
Publication statusPublished - 2014
EventIEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2014 - Kuching, Sarawak, Malaysia
Duration: 1 Dec 20144 Dec 2014
https://ieeexplore.ieee.org/xpl/conhome/7006982/proceeding (Proceedings)

Conference

ConferenceIEEE International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) 2014
Abbreviated titleISPACS 2014
Country/TerritoryMalaysia
CityKuching, Sarawak
Period1/12/144/12/14
Internet address

Keywords

  • computer vision
  • Haar-like feature
  • hand grasping posture

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