var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"http://vclab.science.uoit.ca/faisal-qureshi.bib\",\"jsonp\":\"1\",\"filter\":\"keywords:feet-localization\",\"group0\":\"type\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Giménez\"],\"firstnames\":[\"Cristian\",\"Vilar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Krug\"],\"firstnames\":[\"Silvia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Qureshi\"],\"firstnames\":[\"Faisal\",\"Z.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"O’Nils\"],\"firstnames\":[\"Mattias\"],\"suffixes\":[]}],\"title\":\"Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation\",\"journal\":\"Journal of Imaging\",\"volume\":\"7\",\"year\":\"2021\",\"number\":\"12\",\"article-number\":\"255\",\"url\":\"https://www.mdpi.com/2313-433X/7/12/255\",\"issn\":\"2313-433X\",\"abstract\":\"Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet&mdash;a custom dataset of images of labeled caregiver&rsquo;s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver&rsquo;s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver&rsquo;s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.\",\"doi\":\"10.3390/jimaging7120255\",\"url_paper\":\"pubs/21-jimaging-j2.pdf\",\"keywords\":\"feet-localization\",\"bibtex\":\"@Article{21-jimaging-j2,\\nAUTHOR = {Giménez, Cristian Vilar and Krug, Silvia and Qureshi, Faisal Z. and O’Nils, Mattias},\\nTITLE = {Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation},\\nJOURNAL = {Journal of Imaging},\\nVOLUME = {7},\\nYEAR = {2021},\\nNUMBER = {12},\\nARTICLE-NUMBER = {255},\\nURL = {https://www.mdpi.com/2313-433X/7/12/255},\\nISSN = {2313-433X},\\nABSTRACT = {Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet&mdash;a custom dataset of images of labeled caregiver&rsquo;s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver&rsquo;s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver&rsquo;s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.},\\nDOI = {10.3390/jimaging7120255},\\nurl_Paper = {pubs/21-jimaging-j2.pdf},\\nkeywords = {feet-localization}\\n}\\n\\n\",\"author_short\":[\"Giménez, C. V.\",\"Krug, S.\",\"Qureshi, F. Z.\",\"O’Nils, M.\"],\"key\":\"21-jimaging-j2\",\"id\":\"21-jimaging-j2\",\"bibbaseid\":\"gimnez-krug-qureshi-onils-evaluationof2d3dfeetdetectionmethodsforsemiautonomouspoweredwheelchairnavigation-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.mdpi.com/2313-433X/7/12/255\",\" paper\":\"http://vclab.science.uoit.ca/pubs/21-jimaging-j2.pdf\"},\"keyword\":[\"feet-localization\"],\"metadata\":{\"authorlinks\":{\"giménez, c\":\"http://vclab.science.ontariotechu.ca/projects/feet-localization/feet-localization.html\"}},\"downloads\":2,\"html\":\"\"}],\n      metadata: {\"owner\":\"giménez, c\",\"authorlinks\":{\"giménez, c\":\"http://vclab.science.ontariotechu.ca/projects/feet-localization/feet-localization.html\"}},\n      ownerID: \"GKtNJLHavoXJfMYEP\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  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V.</a>; Krug, S.; Qureshi, F. Z.;  and O’Nils, M.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Imaging</i>, 7(12).  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.mdpi.com/2313-433X/7/12/255\"\n     onclick=\"log_download('gimnez-krug-qureshi-onils-evaluationof2d3dfeetdetectionmethodsforsemiautonomouspoweredwheelchairnavigation-2021', 'https://www.mdpi.com/2313-433X/7/12/255', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://vclab.science.uoit.ca/pubs/21-jimaging-j2.pdf\"\n     onclick=\"log_download('gimnez-krug-qureshi-onils-evaluationof2d3dfeetdetectionmethodsforsemiautonomouspoweredwheelchairnavigation-2021', 'http://vclab.science.uoit.ca/pubs/21-jimaging-j2.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3390/jimaging7120255\"\n     onclick=\"log_download('gimnez-krug-qureshi-onils-evaluationof2d3dfeetdetectionmethodsforsemiautonomouspoweredwheelchairnavigation-2021', 'http://doi.org/10.3390/jimaging7120255', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gimnez-krug-qureshi-onils-evaluationof2d3dfeetdetectionmethodsforsemiautonomouspoweredwheelchairnavigation-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gimnez-krug-qureshi-onils-evaluationof2d3dfeetdetectionmethodsforsemiautonomouspoweredwheelchairnavigation-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@Article{21-jimaging-j2,\nAUTHOR = {Giménez, Cristian Vilar and Krug, Silvia and Qureshi, Faisal Z. and O’Nils, Mattias},\nTITLE = {Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation},\nJOURNAL = {Journal of Imaging},\nVOLUME = {7},\nYEAR = {2021},\nNUMBER = {12},\nARTICLE-NUMBER = {255},\nURL = {https://www.mdpi.com/2313-433X/7/12/255},\nISSN = {2313-433X},\nABSTRACT = {Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet&amp;mdash;a custom dataset of images of labeled caregiver&amp;rsquo;s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver&amp;rsquo;s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver&amp;rsquo;s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.},\nDOI = {10.3390/jimaging7120255},\nurl_Paper = {pubs/21-jimaging-j2.pdf},\nkeywords = {feet-localization}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet&mdash;a custom dataset of images of labeled caregiver&rsquo;s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver&rsquo;s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver&rsquo;s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);