PointingImgEst/index.html at master · EmergentSystemLabStudent/PointingImgEst · GitHub

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<!DOCTYPE html>
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  <meta property="og:description" content="Project page for Pointing Frame Estimation with Audio-Visual Time Series Data for Daily Life Service Robots."/>
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          <h1 class="title is-1 publication-title">Pointing Frame Estimation with Audio-Visual Time Series Data for Daily Life Service Robots</h1>
          <div class="is-size-5 publication-authors">
            <!-- Paper authors -->
            <span class="author-block">
              Hikaru Nakagawa<sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=KPxSCJUAAAAJ&hl=ja&oi=ao" target="_blank">Shoichi Hasegawa<sup>1,*</sup>,</span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=Y4qjYvMAAAAJ&hl=ja&oi=ao" target="_blank">Yoshinobu Hagiwara<sup>1,2</sup>,</a></span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=jtB7J0AAAAAJ&hl=ja&oi=ao" target="_blank">Akira Taniguchi<sup>1</sup>,</a></span>
            <span class="author-block">
              <a href="https://scholar.google.co.jp/citations?user=dPOCLQEAAAAJ&hl=ja&oi=ao" target="_blank">Tadahiro Taniguchi<sup>1,3</sup></a></span>
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              <sup>1</sup>Ritsumeikan University,
              <sup>2</sup>Soka University,
              <sup>3</sup>Kyoto University
              <br><span class="publication-awards">Winner of the Best Paper Award in IEEE SMC 2024 (1st of 755)!</span>
            </span>
            <span class="eql-cntrb"><small><br><sup>*</sup>Corresponding Author</small></span>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Daily life support robots in the home environment interpret the user's pointing and understand the instructions, thereby increasing the number of instructions accomplished.
            This study aims to improve the estimation performance of pointing frames by using speech information when a person gives pointing or verbal instructions to the robot.
            The estimation of the pointing frame, which represents the moment when the user points, can help the user understand the instructions.
            Therefore, we perform pointing frame estimation using a time-series model, utilizing the user's speech, images, and speech-recognized text observed by the robot.
            In our experiments, we set up realistic communication conditions, such as speech containing everyday conversation, non-upright posture, actions other than pointing, and reference objects outside the robot's field of view.
            The results showed that adding speech information improved the estimation performance, especially the Transformer model with Mel-Spectrogram as a feature.
            This study will lead to be applied to object localization and action planning in 3D environments by robots in the future.
          </p>
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      <h2 class="title is-3">Overview</h2>
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          <img src="static/images/overview.svg" alt="image of model"/>
          <h2 class="subtitle has-text-centered">
            An overview of our study. The robot makes an estimation of the moment at which a person gives a pointing instruction.
            The robot uses the speech uttered by the person, the video showing the pointing, and the sentence for the estimation.
          </h2>
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      <h2 class="title is-3">Pointing Frame Estimator with Audio-Visual Time Series Data</h2>
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        <div class="item">
          <img src="static/images/model.svg" alt="image of model"/>
          <h2 class="subtitle has-text-centered">
            An overview of our study. The robot makes an estimation of the moment at which a person gives a pointing instruction.
            The robot uses the speech uttered by the person, the video showing the pointing, and the sentence for the estimation.
          </h2>
        </div>
        <!-- <div class="item">
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      <h2 class="title is-3">Scenarios of Instructions</h2>
      <p class="subtitle has-text-centered">
        We assumed 4 scenarios when the user gives pointing instructions to the robot.
      </p>
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          <!-- Video Carousel -->
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              <h3 class="title is-3">Scenario 1: The verbal utterances include daily conversations</h3>
              <video controls style="width: 700px; height: auto;" preload="metadata">
                <source src="static/videos/other_utterance_except_instruction.mp4" type="video/mp4">
              </video>
            </div>

            <!-- Second Video -->
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              <h3 class="title is-3">Scenario 2: The user's posture is not consistently upright</h3>
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                <source src="static/videos/no_upright.mp4" type="video/mp4">
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            <!-- Third Video -->
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              <h3 class="title is-3">Scenario 3: The user's movements involve actions beyond pointing</h3>
              <video controls style="width: 700px; height: auto;" preload="metadata">
                <source src="static/videos/other_action_except_pointing.mp4" type="video/mp4">
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              <h3 class="title is-3">Scenario 4: The referenced object is outside the robot's visual field</h3>
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                <source src="static/videos/no_reference_object.mp4" type="video/mp4">
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      <h2 class="title is-3">Global localization</h2>
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      <h2 class="title is-3">Pointing frame estimation results</h2>
      <p>
        We used F1 Score as an evaluation item.<br>
        The proposed method achieved a high score when utilizing the combination of a Mel-Spectrogram for feature representation and a Transformer for its architecture.
      </p>
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          <img src="static/images/pointing_frame_result.png" alt="image of representations"/>
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      <iframe  src="static/pdfs/pointingimgest_poster.pdf" width="100%" height="550">
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    <h2 class="title">BibTeX</h2>
    <!-- <pre><code>BibTex Code Here</code></pre> -->
    <pre><code>
      @inproceedings{nakagawa2024pointing,
        author={Nakagawa, Hikaru  and Hasegawa, Shoichi  and Hagiwara, Yoshinobu  and Taniguchi, Akira  and Taniguchi, Tadahiro},
        title={Pointing Frame Estimation with Audio-Visual Time Series Data for Daily Life Service Robots},
        booktitle={IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
        year={2024, in press}
      }
    </code></pre>
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</section>
<!--End BibTex citation -->

<!--Related Research -->
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    <h2 class="title">Related Research</h2>
    <ul>
      <li><a href="https://emergentsystemlabstudent.github.io/MultiViewRetrieve/" target="_blank" rel="noopener noreferrer">SimView</a></li>
      <li><a href="https://emergentsystemlabstudent.github.io/DomainBridgingNav/" target="_blank" rel="noopener noreferrer">CrossIA</a></li>
      <li><a href="https://tomochika-ishikawa.github.io/Active-SpCoSLAM/" target="_blank" rel="noopener noreferrer">Active SpCoSLAM</a></li>
      <li><a href="https://emergentsystemlabstudent.github.io/ECRAP/" target="_blank" rel="noopener noreferrer">ECRAP</a></li>
      <li><a href="https://ieeexplore.ieee.org/abstract/document/10309487" target="_blank" rel="noopener noreferrer">Exophora Resolution Framework</a></li>
    </ul>
  </div>
</section>
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<!--Laboratory Information -->
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      <li><a href="http://www.em.ci.ritsumei.ac.jp/" target="_blank" rel="noopener noreferrer">Emergent Systems Laboratory</a></li>
      <li><a href="https://www.youtube.com/@tarosouhatsu494/videos" target="_blank" rel="noopener noreferrer">Demonstration Videos of the Laboratory</a></li>
    </ul>
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    <h2 class="title">Funding</h2>
    <p>
      This work was supported by JSPS KAKENHI Grants-in-Aid for Scientific Research (Grant Numbers JP23K16975, 22K12212), JST Moonshot Research & Development Program (Grant Number JPMJMS2011), and JST SPRING, Grant Number JPMJSP2101.
    </p>
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