{"id":10396,"date":"2020-09-09T11:48:20","date_gmt":"2020-09-09T09:48:20","guid":{"rendered":"https:\/\/lineact.cesi.fr\/?p=10396"},"modified":"2024-04-04T17:04:24","modified_gmt":"2024-04-04T15:04:24","slug":"inhard-industrial-human-action-recognition-dataset-2","status":"publish","type":"post","link":"https:\/\/lineact.cesi.fr\/en\/inhard-industrial-human-action-recognition-dataset-2\/","title":{"rendered":"INHARD : INdustrial Human Action Recognition Dataset"},"content":{"rendered":"\n<p>&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-group section is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group wrapper__inner is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group editorial__picture editorial__aside editorial__aside--big editorial__aside--picture is-layout-constrained wp-block-group-is-layout-constrained\"><figure class=\"wp-block-post-featured-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset.png\" class=\"attachment-post-thumbnail size-post-thumbnail wp-post-image\" alt=\"inhard dataset\" style=\"object-fit:cover;\" srcset=\"https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset.png 1280w, https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset-480x270.png 480w, https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset-647x364.png 647w, https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset-768x432.png 768w, https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset-288x162.png 288w, https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset-448x252.png 448w, https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset-736x414.png 736w, https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/09\/InHard_dataset-944x531.png 944w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n\n<div class=\"wp-block-group editorial__chapo is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading title--5\"><span><span class=\"icon\" aria-hidden=\"true\"><\/span> In this page\u00a0:<\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"#modalities\">Modalities<\/a><\/li>\n\n\n\n<li><a href=\"#actions\">Actions classes<\/a><\/li>\n\n\n\n<li><a href=\"#experiments\">Experiments and performance metrics<\/a><\/li>\n\n\n\n<li><a href=\"#citations\">Citations<\/a><\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"has-medium-font-size\">We introduce a RGB+S dataset named \u201cIndustrial Human Action Recognition Dataset\u201d (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects. This dataset contains 13 different industrial action classes and over 4800 action samples.<\/p>\n\n\n\n<p class=\"has-medium-font-size\">The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations.<\/p>\n\n\n\n<p class=\"has-medium-font-size\">The dataset is available on <a href=\"https:\/\/github.com\/vhavard\/InHARD\">GitHub<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div id=\"modalities\" class=\"wp-block-group section is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-group wrapper__inner is-layout-flow wp-block-group-is-layout-flow\">\n<h2 class=\"wp-block-heading title--2 is-style-title-cesi-underline\"><span>Modalities<\/span><\/h2>\n\n\n\n<div class=\"wp-block-group editorial__picture editorial__aside is-style-editorial-aside-big editorial__aside--picture is-layout-flow wp-block-group-is-layout-flow\">\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"320\" height=\"319\" src=\"https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/12\/Skeleton-joints-hierarchy.png\" alt=\"\" class=\"wp-image-11936\" style=\"width:320px;height:auto\" srcset=\"https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/12\/Skeleton-joints-hierarchy.png 320w, https:\/\/lineact.cesi.fr\/wp-content\/uploads\/2020\/12\/Skeleton-joints-hierarchy-288x288.png 288w\" sizes=\"auto, (max-width: 320px) 100vw, 320px\" \/><figcaption class=\"wp-element-caption\">To manipulate BVH files, we recommand using the&nbsp;<a href=\"https:\/\/github.com\/omimo\/PyMO\/\">PyMO python library<\/a><\/figcaption><\/figure>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading title--3\"><strong>Skeleton modality<\/strong><\/h3>\n\n\n\n<p><\/p>\n\n\n\n<p>We used a \u201cCombination Perception Neuron 32 Edition v2\u201d motion sensor to capture the skeletal data delivered at a frequency of 120 Hz.<br>Skeleton data comprises :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The 3D locations (Tx, Ty and Tz) of 17 major body joints<\/li>\n\n\n\n<li>The 3 rotations around each axis (Rx, Ry and Rz)<br>Skeleton data are saved into&nbsp;<strong>BVH format<\/strong>&nbsp;files and are stored in the&nbsp;<strong>Skeleton\/ folder<\/strong>&nbsp;of the InHARD dataset.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading title--3\"><strong><strong>Videos modalities<\/strong><\/strong><\/h3>\n\n\n\n<p>We used 3 C920 cameras to capture RGB Data. Each camera captures three different views of the same action. For each setup, two cameras were placed at the same height but at two different horizontal angles: -45\u00b0 and +45\u00b0 to capture both left and right sides. The third camera is placed on top of the subjects to capture the top view.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Camera 1 always observes top views and is displayed on the top left quarter of the RGB video.<\/li>\n\n\n\n<li>Camera 2 observes left side views and is shown on the top right quarter of the RGB video.<\/li>\n\n\n\n<li>Camera 3 observes right side views and is displayed on the bottom right quarter of the RGB video as shown in figure above<\/li>\n<\/ul>\n\n\n\n<p>RGB files are stored in the&nbsp;<strong>RGB\/ folder<\/strong>&nbsp;of the InHARD dataset.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div id=\"actions\" class=\"wp-block-group section has-grey-background-color has-background is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-group wrapper__inner is-layout-flow wp-block-group-is-layout-flow\">\n<h2 class=\"wp-block-heading title--2 is-style-title-cesi-underline\"><span><strong><strong>Actions classes<\/strong><\/strong><\/span><\/h2>\n\n\n\n<p>The list of&nbsp;<strong>13 meta-actions<\/strong>&nbsp;and&nbsp;<strong>74 actions<\/strong>&nbsp;classes are available in the&nbsp;<a href=\"https:\/\/github.com\/vhavard\/InHARD\/blob\/master\/rsc\/Action-Meta-action-list.xlsx\">Action-Meta-action-list.xlsx<\/a>&nbsp;file<\/p>\n\n\n\n<p>Inside the InHARD.zip datatset, you will find the&nbsp;<strong>InHARD.csv<\/strong>&nbsp;file which provides a dataframe with all dataset info including Filename, Subject, Operation, Action low\/high level label, Action start\/end, Duration etc. in order to facilitate the dataset handling and use.<\/p>\n\n\n\n<p><br>We used a software called&nbsp;<a href=\"https:\/\/www.anvil-software.org\/\">ANVIL<\/a>&nbsp;to label our data. You can install it if you want to edit, add or remove actions from actions\u2019 labels files (.anvil) situated at the \/Labels\/ folder.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div id=\"experiments\" class=\"wp-block-group section has-white-background-color has-background is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-group wrapper__inner is-layout-flow wp-block-group-is-layout-flow\">\n<h2 class=\"wp-block-heading title--2 is-style-title-cesi-underline\"><span><strong><strong>Experiments and performance metrics<\/strong><\/strong><\/span><\/h2>\n\n\n\n<p>We propose a set of usage metrics of the InHARD dataset for future utilization. Firstly, we suggest dividing data into two levels; experts and beginners according to subject\u2019s expertise with the manipulation. Thereby, all subjects performing the whole manipulation in less than 6 minutes as average total actions\u2019 duration, are selected as experts. The remaining subjects are categorized as beginners. We define the training and validation sets as follows :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>S_train={P01_R01, P01_R03, P03_R01,&nbsp;<strong>P03_R03<\/strong>, P03_R04, P04_R02, P05_R03, P05_R04, P06_R01,&nbsp;<strong>P07_R01<\/strong>,&nbsp;<strong>P07_R02<\/strong>, P08_R02,&nbsp;<strong>P08_R04<\/strong>,&nbsp;<strong>P09_R01<\/strong>, P09_R03, P10_R01, P10_R02,&nbsp;<strong>P10_R03<\/strong>, P11_R02, P12_R01, P12_R02, P13_R02,&nbsp;<strong>P14_R01<\/strong>,&nbsp;<strong>P15_R01<\/strong>,&nbsp;<strong>P15_R02<\/strong>, P16_R02}<\/li>\n\n\n\n<li>S_val={P01_R02, P02_R01,&nbsp;<strong>P02_R02<\/strong>, P04_R01, P05_R01, P05_R02, P08_R01,&nbsp;<strong>P08_R03<\/strong>,&nbsp;<strong>P09_R02<\/strong>, P11_R01,&nbsp;<strong>P14_R02<\/strong>, P16_R01}<\/li>\n<\/ul>\n\n\n\n<p><strong>PS<\/strong>&nbsp;: Samples in bold are selected as Experts. The remaining are selected as begin<\/p>\n<\/div>\n<\/div>\n\n\n\n<div id=\"citations\" class=\"wp-block-group section has-grey-background-color has-background is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-group wrapper__inner is-layout-flow wp-block-group-is-layout-flow\">\n<h2 class=\"wp-block-heading title--2 is-style-title-cesi-underline\"><span><strong><strong>Citations<\/strong><\/strong><\/span><\/h2>\n\n\n\n<p>To cite this work, please use:<br>@InProceedings{inhard2020ichms,<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;author = {Mejdi DALLEL, Vincent HAVARD, David BAUDRY, Xavier SAVATIER},<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;title = {An Industrial Human Action Recogniton Dataset in the Context of Industrial Collaborative Robotics},<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;booktitle = {IEEE International Conference on Human-Machine Systems ICHMS},<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;month = Postponed due to COVID-19,<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;year = {2020},<br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;url = {https:\/\/github.com\/vhavard\/InHARD}<br>}<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>We introduce a RGB+S dataset named \u201cIndustrial Human Action Recognition Dataset\u201d (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects.<\/p>\n","protected":false},"author":78,"featured_media":10397,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[466],"tags":[],"class_list":["post-10396","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-actualites"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - 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