Gestures In-The-Wild: Detecting Conversational Hand Gestures in Crowded Scenes Using a Multimodal Fusion of Bags of Video Trajectories and Body Worn Acceleration

Laura Cabrera Quiros*, David M.J. Tax, Hayley Hung

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
4 Downloads (Pure)

Abstract

This paper addresses the detection of hand gestures during free-standing conversations in crowded mingle scenarios. Unlike the scenarios of the previous works in gesture detection and recognition, crowded mingle scenes have additional challenges such as cross-contamination between subjects, strong occlusions, and nonstationary backgrounds. This makes them more complex to analyze using computer vision techniques alone. We propose a multimodal approach using video and wearable acceleration data recorded via smart badges hung around the neck. In the video modality, we propose to treat noisy dense trajectories as bags-of-trajectories. For a given bag, we can have good trajectories corresponding to the subject, and bad trajectories due for instance to cross-contamination. However, we hypothesize that for a given class, it should be possible to learn trajectories that are discriminative while ignoring noisy trajectories. We do this by exploiting multiple instance learning via embedded instance selection as our multiple instance learning approach. This technique also allows us to identify which instances contribute more to the classification. By fusing the decisions of the classifiers from the video and wearable acceleration modalities, we show improvements over the unimodal approaches with an AUC of 0.69. We also present a static analysis and a dynamic analysis to assess the impact of noisy data on the fused detection results, showing that the moments of high occlusion in the video are compensated by the information from the wearables. Finally, we applied our method to detect speaking status, leveraging the close relationship found in the literature between hand gestures and speech.

Original languageEnglish
Article number8734888
Pages (from-to)138-147
Number of pages10
JournalIEEE Transactions on Multimedia
Volume22
Issue number1
DOIs
Publication statusPublished - 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • crowded mingles
  • dense trajectories
  • Hand gestures
  • MILES
  • multiple instance learning
  • wearable acceleration

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