{"id":1777,"date":"2022-03-16T04:57:39","date_gmt":"2022-03-16T11:57:39","guid":{"rendered":"https:\/\/www.icpr2022.com\/?page_id=1777"},"modified":"2022-08-18T05:33:28","modified_gmt":"2022-08-18T12:33:28","slug":"icpr-2022-challenges","status":"publish","type":"page","link":"https:\/\/www.icpr2022.com\/icpr-2022-challenges\/","title":{"rendered":"ICPR 2022 Challenges"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1777\" class=\"elementor elementor-1777\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-32d41b2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"32d41b2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5a3c955\" data-id=\"5a3c955\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2c31b50 elementor-widget elementor-widget-text-editor\" data-id=\"2c31b50\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.16.0 - 17-10-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>All sessions to take place on <b>Sunday, 21 August.&nbsp;<\/b><br><b>Virtual session: 9am-1pm<\/b><br><b>In-person session: 2pm-5pm<\/b><\/p><b>\n<\/b><p><b><\/b><em>All times shown are in Canadian EDT or GMT-4. Convert to your time zone <a href=\"https:\/\/www.timeanddate.com\/worldclock\/converter.html\" target=\"_blank\" rel=\"noopener open\">here<\/a>.<\/em><b><br><\/b><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-74d8fdd elementor-widget elementor-widget-shortcode\" data-id=\"74d8fdd\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><div id=\"footable_parent_1779\"\n         class=\" footable_parent ninja_table_wrapper loading_ninja_table wp_table_data_press_parent semantic_ui colored_table\">\n                <table data-ninja_table_instance=\"ninja_table_instance_0\" data-footable_id=\"1779\" data-filter-delay=\"1000\" aria-label=\"ICPR 2022 Challenges\"            id=\"footable_1779\"\n           data-unique_identifier=\"ninja_table_unique_id_1093642960_1779\"\n           class=\" foo-table ninja_footable foo_table_1779 ninja_table_unique_id_1093642960_1779 ui table tablita nt_type_legacy_table striped vertical_centered ninja_custom_color inverted footable-paging-right hide_all_borders ninja_table_search_disabled ninja_table_pro\">\n                <colgroup>\n                            <col class=\"ninja_column_0 \">\n                            <col class=\"ninja_column_1 \">\n                            <col class=\"ninja_column_2 \">\n                    <\/colgroup>\n        <thead>\n<tr class=\"footable-header\">\n                                                        <th scope=\"col\"  class=\"ninja_column_0 ninja_clmn_nm_title \">Name \/ Acronym<\/th><th scope=\"col\"  class=\"ninja_column_1 ninja_clmn_nm_workshops \">Organizers (with affiliation)<\/th><th scope=\"col\"  class=\"ninja_column_2 ninja_clmn_nm_tutorials \">Short Description + Link to the Challenge Web<\/th><\/tr>\n<\/thead>\n<tbody>\n\n        <tr data-row_id=\"72\" class=\"ninja_table_row_0 nt_row_id_72\">\n            <td>ODeuropa Competition on Olfactory Object Recognition - ODOR<\/td><td><i>Pattern Recognition Lab, Friedrich-Alexander-Universit\u00a8at Erlangen-N\u00a8urnberg, Germany<\/i><br>Mathias Zinnen, Prathmesh Madhu, Ronak Kosti, Andreas Maier, and Vincent Christlein<br> <br><i> German Studies and Arts, Philipps-Universitat Marburg, Germany<\/i><br>Peter Bell<\/td><td>The Odeuropa Competition on Olfactory Object Recognition aims to foster the development of object detection in the visual arts, and to promote an olfactory perspective on digital heritage. Object detection on historical artworks is a particularly challenging task because it has to cope with a great variance of styles and artistic periods. The task is furthermore complicated by the particularity and historical variance of our target objects which exhibit a large intra-class variance. These challenges encourage participants to create innovative approaches of fewshot learning or domain adaptation. We provide a dataset of about 3000 artworks annotated with 30 000 tightly fit bounding boxes that will be split in a public training set, a validation set, and a private test set. <br><a href=\"https:\/\/odor-challenge.odeuropa.eu\/\" target=\"blank\">Take me there!<\/a><\/td>        <\/tr>\n            <tr data-row_id=\"85\" class=\"ninja_table_row_1 nt_row_id_85\">\n            <td>Multimodal Subtitle Recognition - MSR<\/td><td><i>Tencent<\/i><br>Shan Huang, Shen Huang, Pengfei Hu, Xiang Wang, Jian Kang, Li Lu <\/i><br><br><i>South China University of Technology<\/i><br>Lianwen Jin<i><br><br>The Chinese University of Hong Kong<\/i><br>YuLiang Liu <i><br><br>Lenovo<\/i><br>Yaqiang Wu <\/td><td>In recent years, with the rapid rise of short videos, live broadcasts and other media-based applications, the widespread transmission of video data has led to a significant increase in user-generated content. A wide variety of creative platforms and pattern have emerged, and media publication criteria is becoming more and more civilian, leading to more complex and dynamic acoustic scenes in various long or short video and live streaming. The problems of video subtitle recognition and speech recognition under various scenarios have been of considerable concern by researchers. The development of methods accurately recognize and understand various types of video content has become an indispensable tool in downstream applications such as subtitle creation, content recommendation, digital media archival, and so on. In this challenge, we focus on extracting subtitles from videos using both visual and audio modalities. Most previous works exploit it with a single modality , and each modality has its own advantages and annotation difficulties on various types of training data. To this end, we present tasks that explore the integration of the advantages of both video and audio modalitioes. We expect to obtain high precision subtitles with lower annotation costs, thus expanding the scope of practical applications. <br><a href=\"https:\/\/icprmsr.github.io\/\" target=\"blank\" rel=\"noopener\">Take me there!<\/a><\/td>        <\/tr>\n            <tr data-row_id=\"70\" class=\"ninja_table_row_2 nt_row_id_70\">\n            <td>Face Recognition Under Drone Surveillance Concerning Turbulence - FaceDrone<\/td><td><i>University at Buffalo, NY, USA<\/i> Nalini Ratha, Akshay Agarwal<i><br><br>IIT Jodhpur, India<\/i> Mayank Vatsa, Richa Singh<\/td><td>Identification of a person at a remote location and crowded locations demand the deployment of unmanned aerial vehicles such as drones. To maintain the safety concern of both the humans and the vehicle, these vehicles are always placed at a certain distance from each other which is generally larger in coveted scenarios. Therefore, the captured images reflect the following characteristics: (i) low resolution, (ii) low quality due to camera functionality and impact of weather conditions and motion of the vehicle. In this, the effect of the weather conditions is critical to consider for effective face recognition in drone surveillance. In this challenge, for the first time, we will release the drone face images reflecting turbulence-like effects which make not only the problem interesting but complicated enough to solve. <br><a href=\"http:\/\/iab-rubric.org\/DroneFace2022\/index.html\" target=\"blank\">Take me there!<\/a><\/td>        <\/tr>\n            <tr data-row_id=\"71\" class=\"ninja_table_row_3 nt_row_id_71\">\n            <td>Competition on HArvesting Raw Tables from Infographics - CHART-Infographics<\/td><td><i>Facultad de Ingenieria, Universidad Tecnol\u00f3gica Centroamericana<\/i><br> Kenny Davila, David A. Mendoza<i><br><br>Department of Computer Science and Engineering, University at Buffalo<\/i><br>Saleem Ahmed, Fei Xu, Srirangaraj Setlur, and Venu Govindaraju<\/td><td>Charts are a compact method of displaying and comparing data. Automatically extracting data from charts is a key step in understanding the intent behind a chart which could lead to a better understanding of the document itself. The goal of the third edition of the Competition on Harvesting Raw Tables from Infographics (ICPR 2022 CHART-Infographics) is to provide a standard benchmark for methods that enable recognition and recognition of statistical charts. We divide the process of chart recognition into multiple tasks: chart image classification (Task 1), text detection and recognition (Task 2), text role classification (Task 3), axis analysis (Task 4), legend analysis (Task 5), plot element detection and classification (Task 6.a), data extraction (Task 6.b), and end-to-end data extraction (Task 7).  Previous editions of CHART-Infographics  generated publicly available chart datasets (real and synthetic) as well as open-source tools for chart annotation and evaluation of chart recognition models. Our data and tools have been used not only by competition participants, but also by multiple recent published works in the field. The third edition builds on our previous efforts by improving existing tools and datasets. We provide much larger datasets of real charts including a brand new test set with special focus on previously under-represented chart types. <br><a href=\"https:\/\/chartinfo.github.io\/\" target=\"blank\">Take me there!<\/a><\/td>        <\/tr>\n            <tr data-row_id=\"73\" class=\"ninja_table_row_4 nt_row_id_73\">\n            <td>Real-World Video Understanding for Urban Pipe Inspection - VideoPipe<\/td><td><i>Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China  Shanghai Artificial Intelligence Laboratory, China<\/i><br>Yali Wang, Yu Qiao <i><br><br>Shenzhen Bominwell Robotics Co.,Ltd, China<\/i><br>Yi Dai, Fei Xie<\/td><td>Video understanding is an important problem in computer vision. Currently, the well-studied task in this problem is human action recognition, where the clips are manually trimmed from the long videos, and a single class of human action is assumed for each clip. However, we may face more complicated scenarios in the industrial applications. For example, in the real-world urban pipe system, anomaly defects are fine-grained, multi-labeled, domain-relevant. To recognize them correctly, we need to understand the detailed video content. For this reason, we propose to advance research areas of video understanding, with a shift from traditional action recognition to industrial anomaly analysis. In particular, we introduce two high-quality video benchmarks, namely QV-Pipe and CCTV-Pipe, for anomaly inspection in the real-world urban pipe systems. Based on these new datasets, we will host two competitions to bring new opportunities and challenges for video understanding in smart city and beyond.<br><a href=\"https:\/\/videopipe.github.io\/\" target=\"blank\">Take me there!<\/a><\/td>        <\/tr>\n            <tr data-row_id=\"74\" class=\"ninja_table_row_5 nt_row_id_74\">\n            <td>Moving Object Detection and Tracking in Satellite Videos - SatVideoDT<\/td><td><i>National University of Defense Technology<\/i><br>Yulan Guo, Qian Yin, Feng Zhang <i>Ye Zhang<br><br>University of Oxford<\/i><br> Qingyong Hu<\/td><td>Satellite video cameras can provide continuous observation for a large-scale area, which is suitable for several downstream remote sensing applications including traffic management, ocean monitoring, and smart city. Recently, moving objects detection and tracking in satellite videos have attracted increasing attention in both academia and industry. However, it remains challenging to achieve accurate and robust moving object detection and tracking in satellite videos, due to the lack of high-quality and well-annotated public datasets and comprehensive benchmarks for performance evaluation. To this end, we plan to organize a challenge based on the recent VISO dataset, and focus on the specific challenges and research problems in moving object detection and tracking in satellite videos. We hope this challenge could inspire the community to explore the tough problems in satellite video analysis, and ultimately drive technological advancement in emerging applications.<br><a href=\"https:\/\/satvideodt.github.io\/\" target=\"blank\">Take me there!<\/a><\/td>        <\/tr>\n            <tr data-row_id=\"75\" class=\"ninja_table_row_6 nt_row_id_75\">\n            <td>Detection of wastewater contaminants through low cost sensors: a multi-class problem - WaterContaminants<\/td><td><i>Scientific laboratories of the Polish police, Poland<\/i><br> Piotr Olejnik<i><br><br>Central Forensic Laboratory of the Police (Poland)<\/i><br>Anna Trynda<br><br><i>University of Cassino and Southern Lazio (Italy)<\/i><br>Mario Molinara, Luca Gerevini, Luigi Ferrigno, Carmine Bourelly<br><br><i>Sensichips s.r.l.<\/i><br>Luca Ruscitti, Francesco Magliocca<\/td><td>Water pollution caused by human activities poses a serious global threat to human health. Sensor technologies enabling water monitoring are an important tool that can help facing this problem. In this challenge, we propose a dataset acquired in different laboratory around the Europe through a proprietary sensor technology for the detection and recognition of fourteen water contaminants. The system architecture of the acquisition system is composed of two layers: (i) a sensing layer based on the SENSIPLUS chip (customized in a commercial solution called Smart Cable Water - SCW) with different interdigitated electrodes metalized through different materials; and (ii) a data collection and communication layer with both hardware and software components. <br><a href=\"https:\/\/aida.unicas.it\/icprchallenge2022\/\" target=\"blank\">Take me there!<\/a><\/td>        <\/tr>\n    <\/tbody><!--ninja_tobody_rendering_done-->\n    <\/table>\n                    <style type=\"text\/css\" id='ninja_table_custom_css_1779'>\n                        #footable_1779  {\n    font-family: inherit;\n    font-size: 14px;\n    }\n\n\n    #footable_1779 tbody tr td span.fooicon-plus:before {\n    background-color:  !important;\n    }\n    #footable_1779 tbody tr td span.fooicon-minus:before {\n    background-color:  !important;\n    }\n\n    #footable_1779 tbody tr:hover td span.fooicon-plus:before {\n    background-color:  !important;\n    }\n    #footable_1779 tbody tr:hover td span.fooicon-minus:before {\n    background-color:  !important;\n    }\n\n    #footable_1779 thead tr.footable-header th span::before {\n    background-color: rgba(255, 255, 255, 1) !important;\n    }\n    #footable_1779,\n    #footable_1779 table {\n    background-color:  !important;\n    color:  !important;\n    border-color:  !important;\n    }\n    #footable_1779 thead tr.footable-filtering th {\n    background-color:  !important;\n    color:  !important;\n    }\n    #footable_1779:not(.hide_all_borders) thead tr.footable-filtering th {\n            border : 1px solid transparent !important;\n        }\n    #footable_1779 .input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle) {\n    background-color:  !important;\n    color:  !important;\n    }\n    #footable_1779 tr.footable-header, #footable_1779 tr.footable-header th, .colored_table #footable_1779 table.ninja_table_pro.inverted.table.footable-details tbody tr th {\n    background-color: rgba(16, 208, 222, 1) !important;\n    color: rgba(255, 255, 255, 1) !important;\n    }\n    \n        #footable_1779 tbody tr:hover {\n    background-color:  !important;\n    color:  !important;\n    }\n    #footable_1779 tbody tr:hover td {\n    border-color:  !important;\n    }\n            #footable_1779 tbody tr:nth-child(even) {\n        background-color: rgba(250, 250, 250, 1) !important;\n        color:  !important;\n        }\n        #footable_1779 tbody tr:nth-child(odd) {\n        background-color: rgba(255, 123, 71, 0.4) !important;\n        color:  !important;\n        }\n        #footable_1779 tbody tr:nth-child(even):hover {\n        background-color:  !important;\n        }\n        #footable_1779 tbody tr:nth-child(odd):hover {\n        background-color:  !important;\n        }\n\n        #footable_1779 tbody tr:nth-child(even) td span.fooicon-plus:before {\n        background-color:  !important;\n        }\n        #footable_1779 tbody tr:nth-child(even) td span.fooicon-minus:before {\n        background-color:  !important;\n        }\n\n        #footable_1779 tbody tr:nth-child(odd) td span.fooicon-plus:before {\n        background-color:  !important;\n        }\n        #footable_1779 tbody tr:nth-child(odd) td span.fooicon-minus:before {\n        background-color:  !important;\n        }\n\n        #footable_1779 tbody tr:nth-child(even) tr:hover td span.fooicon-plus:before {\n        background-color:  !important;\n        }\n        #footable_1779 tbody tr:nth-child(even) tr:hover td span.fooicon-minus:before {\n        background-color:  !important;\n        }\n\n        #footable_1779 tbody tr:nth-child(odd) tr:hover td span.fooicon-plus:before {\n        background-color:  !important;\n        }\n        #footable_1779 tbody tr:nth-child(odd) tr:hover td span.fooicon-minus:before {\n        background-color:  !important;\n        }\n    \n    #footable_1779 tfoot .footable-paging {\n    background-color:  !important;\n    }\n    #footable_1779 tfoot .footable-paging .footable-page.active a {\n    background-color:  !important;\n    }\n    #footable_1779:not(.hide_all_borders) tfoot tr.footable-paging td {\n    border-color:  !important;\n    }\n    \ntable {  \n    border-spacing: .25em !important;\n}\n\ntd.ninja_column_0.ninja_clmn_nm_title {\n    font-weight: bold;\n}\n\n#footable_parent_1779  thead th.ninja_column_0.ninja_clmn_nm_title {\n    background: white !important;\n}\n\n\n#footable_parent_1779  thead th.ninja_column_1.ninja_clmn_nm_date {\n    border-radius: .28571429rem 0  0 0 !important;\n}#footable_1779 th.ninja_column_0 { text-align: center; }#footable_1779 td.ninja_column_1 { text-align: left; }#footable_1779 th.ninja_column_1 { text-align: center; }#footable_1779 td.ninja_column_2 { text-align: left; }#footable_1779 th.ninja_column_2 { text-align: center; }                <\/style>\n                \n    \n    \n<\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>All sessions to take place on Sunday, 21 August.&nbsp;Virtual session: 9am-1pmIn-person session: 2pm-5pm All times shown are in Canadian EDT or GMT-4. Convert to your time zone here.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"plain-container","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"","footnotes":""},"_links":{"self":[{"href":"https:\/\/www.icpr2022.com\/wp-json\/wp\/v2\/pages\/1777"}],"collection":[{"href":"https:\/\/www.icpr2022.com\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.icpr2022.com\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.icpr2022.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.icpr2022.com\/wp-json\/wp\/v2\/comments?post=1777"}],"version-history":[{"count":34,"href":"https:\/\/www.icpr2022.com\/wp-json\/wp\/v2\/pages\/1777\/revisions"}],"predecessor-version":[{"id":2980,"href":"https:\/\/www.icpr2022.com\/wp-json\/wp\/v2\/pages\/1777\/revisions\/2980"}],"wp:attachment":[{"href":"https:\/\/www.icpr2022.com\/wp-json\/wp\/v2\/media?parent=1777"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}