{"id":62507,"date":"2020-01-24T16:56:21","date_gmt":"2020-01-24T15:56:21","guid":{"rendered":"http:\/\/www.prophesee.ai\/?p=62507"},"modified":"2023-01-21T16:39:54","modified_gmt":"2023-01-21T08:39:54","slug":"prophesee-gen1-automotive-detection-dataset","status":"publish","type":"post","link":"https:\/\/www.prophesee-cn.com\/en\/2020\/01\/24\/prophesee-gen1-automotive-detection-dataset\/","title":{"rendered":"Prophesee Gen1 Automotive Detection Dataset"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; fullwidth=&#8221;on&#8221; _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_fullwidth_code _builder_version=&#8221;4.16&#8243; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;]\n\t\t\t<!-- START ATIS AUTOMOTIVE DETECTION DATASET HEADER REVOLUTION SLIDER 6.7.34 --><p class=\"rs-p-wp-fix\"><\/p>\n\t\t\t<rs-module-wrap id=\"rev_slider_72_1_wrapper\" data-source=\"gallery\" style=\"visibility:hidden;background:rgba(255,255,255,0);padding:0;margin:0px auto;margin-top:-70px;margin-bottom:0px;\">\n\t\t\t\t<rs-module id=\"rev_slider_72_1\" style=\"\" data-version=\"6.7.34\">\n\t\t\t\t\t<rs-slides style=\"overflow: hidden; position: absolute;\">\n\t\t\t\t\t\t<rs-slide style=\"position: absolute;\" data-key=\"rs-110\" data-title=\"Intro\" data-anim=\"ms:3000;\" data-in=\"o:-0.5;sx:1.6;sy:1.6;e:power3.inOut;\" data-out=\"o:0;sx:0.6;sy:0.6;e:power3.inOut;\">\n\t\t\t\t\t\t\t<img decoding=\"async\" src=\"\/\/www.prophesee-cn.com\/wp-content\/plugins\/revslider\/sr6\/assets\/assets\/dummy.png\" alt=\"\" title=\"ATIS-Automotive-detection-dataset-thumbnail\" width=\"1800\" height=\"1065\" class=\"rev-slidebg tp-rs-img rs-lazyload\" data-lazyload=\"\/\/www.prophesee-cn.com\/wp-content\/uploads\/2023\/01\/ATIS-Automotive-detection-dataset-thumbnail.jpg\" data-parallax=\"10\" data-no-retina>\n<!--\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-72-slide-110-layer-1\" \n\t\t\t\t\t\t\t\tdata-type=\"text\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;xo:0,0,0,2px;y:m;yo:30px,-23px,-31px,22px;\"\n\t\t\t\t\t\t\t\tdata-text=\"w:normal;s:50,40,45,35;l:50,50,50,40;fw:700;a:center;\"\n\t\t\t\t\t\t\t\tdata-dim=\"w:506px,363px,295px,291px;\"\n\t\t\t\t\t\t\t\tdata-rsp_bd=\"off\"\n\t\t\t\t\t\t\t\tdata-frame_1=\"sR:10;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8690;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:5;background-color:rgba(193,207,221,0);font-family:'Montserrat';\"\n\t\t\t\t\t\t\t>PROPHESEE GEN1 \nAUTOMOTIVE\nDETECTION\nDATASET \n\t\t\t\t\t\t\t<\/rs-layer><!--\n\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-72-slide-110-layer-2\" \n\t\t\t\t\t\t\t\tclass=\"tp-shape tp-shapewrapper\"\n\t\t\t\t\t\t\t\tdata-type=\"shape\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;y:m,t,t,b;yo:160px,351px,245px,88px;\"\n\t\t\t\t\t\t\t\tdata-text=\"a:center;\"\n\t\t\t\t\t\t\t\tdata-dim=\"w:111px;h:7px;\"\n\t\t\t\t\t\t\t\tdata-frame_1=\"sR:10;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8690;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:6;background-color:#ffffff;\"\n\t\t\t\t\t\t\t> \n\t\t\t\t\t\t\t<\/rs-layer><!--\n-->\t\t\t\t\t<\/rs-slide>\n\t\t\t\t\t<\/rs-slides>\n\t\t\t\t<\/rs-module>\n\t\t\t\t<script>\n\t\t\t\t\tsetREVStartSize({c: 'rev_slider_72_1',rl:[1240,1024,778,480],el:[650,500,350,450],gw:[1200,600,700,480],gh:[650,500,350,450],type:'hero',justify:'',layout:'fullwidth',mh:\"0\"});if (window.RS_MODULES!==undefined && window.RS_MODULES.modules!==undefined && window.RS_MODULES.modules[\"revslider721\"]!==undefined) {window.RS_MODULES.modules[\"revslider721\"].once = false;window.revapi72 = undefined;if (window.RS_MODULES.checkMinimal!==undefined) window.RS_MODULES.checkMinimal()}\n\t\t\t\t<\/script>\n\t\t\t<\/rs-module-wrap>\n\t\t\t<!-- END REVOLUTION SLIDER -->\n[\/et_pb_fullwidth_code][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.16&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; custom_padding=&#8221;||0px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text _builder_version=&#8221;4.16&#8243; text_font=&#8221;Montserrat||||||||&#8221; text_text_color=&#8221;#1e2534&#8243; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h1 style=\"text-align: center;\"><span><\/span><span class=\"c-mrkdwn__br\" data-stringify-type=\"paragraph-break\"><\/span><span>Download Prophesee\u2019s GEN1 Automotive Detection Dataset,<\/span><br \/><span>the largest Event-Based Dataset to date.<\/span>\u00a0<\/h1>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; fullwidth=&#8221;on&#8221; _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_fullwidth_code _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;]\n\t\t\t<!-- START Dataset ATIS Automotive Detection banner REVOLUTION SLIDER 6.7.34 --><p class=\"rs-p-wp-fix\"><\/p>\n\t\t\t<rs-module-wrap id=\"rev_slider_73_2_wrapper\" data-source=\"gallery\" style=\"visibility:hidden;background:transparent;padding:0;margin:0px auto;margin-top:0;margin-bottom:0;\">\n\t\t\t\t<rs-module id=\"rev_slider_73_2\" style=\"\" data-version=\"6.7.34\">\n\t\t\t\t\t<rs-slides style=\"overflow: hidden; position: absolute;\">\n\t\t\t\t\t\t<rs-slide style=\"position: absolute;\" data-key=\"rs-111\" data-title=\"Slide\" data-link=\"\/\/www.prophesee.ai\/recognition\/\" data-tag=\"l\" data-target=\"_blank\" data-seoz=\"front\" data-in=\"o:0;\" data-out=\"a:false;\">\n\t\t\t\t\t\t\t<img decoding=\"async\" src=\"\/\/www.prophesee-cn.com\/wp-content\/plugins\/revslider\/sr6\/assets\/assets\/dummy.png\" alt=\"\" title=\"Banner-kpi\" width=\"2000\" height=\"1333\" class=\"rev-slidebg tp-rs-img rs-lazyload\" data-lazyload=\"\/\/www.prophesee-cn.com\/wp-content\/uploads\/2023\/01\/Banner-kpi.jpg\" data-parallax=\"14\" data-panzoom=\"d:5000;e:power3.out;ss:130;se:100;\" data-no-retina>\n<!--\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-73-slide-111-layer-7\" \n\t\t\t\t\t\t\t\tclass=\"tp-shape tp-shapewrapper\"\n\t\t\t\t\t\t\t\tdata-type=\"shape\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;y:m;\"\n\t\t\t\t\t\t\t\tdata-text=\"fw:500;a:inherit;\"\n\t\t\t\t\t\t\t\tdata-dim=\"w:100%;h:100%;\"\n\t\t\t\t\t\t\t\tdata-basealign=\"slide\"\n\t\t\t\t\t\t\t\tdata-rsp_o=\"off\"\n\t\t\t\t\t\t\t\tdata-rsp_bd=\"off\"\n\t\t\t\t\t\t\t\tdata-frame_1=\"e:none;sp:0;sR:10;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;e:power3.out;st:250;sp:3000;sR:240;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:6;background-color:#ffffff;\"\n\t\t\t\t\t\t\t> \n\t\t\t\t\t\t\t<\/rs-layer><!--\n\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-73-slide-111-layer-13\" \n\t\t\t\t\t\t\t\tclass=\"rs-pxl-1\"\n\t\t\t\t\t\t\t\tdata-type=\"text\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;xo:-362px,-329px,-226px,-144px;y:m,m,t,t;yo:-43px,-38px,72px,89px;\"\n\t\t\t\t\t\t\t\tdata-text=\"s:80,70,50,40;l:90,90,80,55;fw:700,700,700,800;a:inherit;\"\n\t\t\t\t\t\t\t\tdata-frame_1=\"sR:10;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8690;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:9;font-family:'Montserrat';\"\n\t\t\t\t\t\t\t>228,123 \n\t\t\t\t\t\t\t<\/rs-layer><!--\n\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-73-slide-111-layer-14\" \n\t\t\t\t\t\t\t\tclass=\"rs-pxl-1\"\n\t\t\t\t\t\t\t\tdata-type=\"text\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;xo:-360px,-326px,-231px,-144px;y:m,m,t,t;yo:49px,47px,171px,159px;\"\n\t\t\t\t\t\t\t\tdata-text=\"s:30,30,25,20;l:30,30,30,25;fw:600,600,700,700;a:center;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8700;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:10;font-family:'Montserrat';\"\n\t\t\t\t\t\t\t>CARS \n\t\t\t\t\t\t\t<\/rs-layer><!--\n\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-73-slide-111-layer-15\" \n\t\t\t\t\t\t\t\tclass=\"rs-pxl-1\"\n\t\t\t\t\t\t\t\tdata-type=\"text\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:l,c,c,c;xo:585px,11px,0,0;y:m,m,t,t;yo:-43px,-38px,68px,86px;\"\n\t\t\t\t\t\t\t\tdata-text=\"s:80,70,50,40;l:90,90,80,55;fw:700,700,700,800;a:inherit;\"\n\t\t\t\t\t\t\t\tdata-frame_1=\"sR:10;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8690;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:7;font-family:'Montserrat';\"\n\t\t\t\t\t\t\t>39 \n\t\t\t\t\t\t\t<\/rs-layer><!--\n\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-73-slide-111-layer-16\" \n\t\t\t\t\t\t\t\tclass=\"rs-pxl-1\"\n\t\t\t\t\t\t\t\tdata-type=\"text\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;xo:14px,10px,2px,0;y:m,m,t,t;yo:49px,46px,171px,161px;\"\n\t\t\t\t\t\t\t\tdata-text=\"s:30,30,25,20;l:30,30,30,25;fw:600,600,700,700;a:center;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8700;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:8;font-family:'Montserrat';\"\n\t\t\t\t\t\t\t>HOURS <\/br>  \n\t\t\t\t\t\t\t<\/rs-layer><!--\n\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-73-slide-111-layer-17\" \n\t\t\t\t\t\t\t\tclass=\"rs-pxl-1\"\n\t\t\t\t\t\t\t\tdata-type=\"text\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:l,c,c,c;xo:880px,304px,240px,138px;y:m,m,m,t;yo:-43px,-38px,-38px,88px;\"\n\t\t\t\t\t\t\t\tdata-text=\"s:80,70,50,40;l:90,90,80,55;fw:700,700,700,800;a:inherit;\"\n\t\t\t\t\t\t\t\tdata-frame_1=\"sR:10;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8690;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:9;font-family:'Montserrat';\"\n\t\t\t\t\t\t\t>27,658 \n\t\t\t\t\t\t\t<\/rs-layer><!--\n\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-73-slide-111-layer-18\" \n\t\t\t\t\t\t\t\tclass=\"rs-pxl-1\"\n\t\t\t\t\t\t\t\tdata-type=\"text\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;xo:392px,316px,238px,138px;y:m,m,t,t;yo:49px,48px,171px,160px;\"\n\t\t\t\t\t\t\t\tdata-text=\"s:30,30,25,20;l:30,30,30,25;fw:600,600,700,700;a:center;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8700;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:10;font-family:'Montserrat';\"\n\t\t\t\t\t\t\t>PEDESTRIANS \n\t\t\t\t\t\t\t<\/rs-layer><!--\n\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-73-slide-111-layer-23\" \n\t\t\t\t\t\t\t\tclass=\"tp-iefixlayer tp-shape tp-shapewrapper\"\n\t\t\t\t\t\t\t\tdata-type=\"shape\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;y:m;\"\n\t\t\t\t\t\t\t\tdata-text=\"fw:100;a:inherit;\"\n\t\t\t\t\t\t\t\tdata-dim=\"w:100%;h:100%;\"\n\t\t\t\t\t\t\t\tdata-vbility=\"f,f,f,f\"\n\t\t\t\t\t\t\t\tdata-basealign=\"slide\"\n\t\t\t\t\t\t\t\tdata-frame_1=\"sR:10;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8690;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:5;background-color:rgba(204,204,204,0.75);\"\n\t\t\t\t\t\t\t> \n\t\t\t\t\t\t\t<\/rs-layer><!--\n-->\t\t\t\t\t<\/rs-slide>\n\t\t\t\t\t<\/rs-slides>\n\t\t\t\t<\/rs-module>\n\t\t\t\t<script>\n\t\t\t\t\tsetREVStartSize({c: 'rev_slider_73_2',rl:[1240,1024,778,480],el:[300,300,300,300],gw:[1240,1024,778,480],gh:[300,300,300,300],type:'hero',justify:'',layout:'fullwidth',mh:\"0\"});if (window.RS_MODULES!==undefined && window.RS_MODULES.modules!==undefined && window.RS_MODULES.modules[\"revslider732\"]!==undefined) {window.RS_MODULES.modules[\"revslider732\"].once = false;window.revapi73 = undefined;if (window.RS_MODULES.checkMinimal!==undefined) window.RS_MODULES.checkMinimal()}\n\t\t\t\t<\/script>\n\t\t\t<\/rs-module-wrap>\n\t\t\t<!-- END REVOLUTION SLIDER -->\n[\/et_pb_fullwidth_code][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;0px||0px|||&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.16&#8243; background_color=&#8221;#1e2534&#8243; custom_padding=&#8221;54px|0px|40px|0px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;9px|0px|0px|0px||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text _builder_version=&#8221;4.16&#8243; text_font=&#8221;||||||||&#8221; text_text_color=&#8221;#ffffff&#8221; text_font_size=&#8221;18px&#8221; inline_fonts=&#8221;Montserrat&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>The dataset was recorded using a\u00a0<b data-stringify-type=\"bold\">PROPHESEE GEN1 sensor<\/b>\u00a0with a resolution of <strong>304&#215;240 pixels<\/strong>, mounted on a car dashboard. The labels were obtained using the gray level estimation feature of the <a href=\"http:\/\/neuromorphic-vision.com\/public\/publications\/21\/publication.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">ATIS<\/a> camera by labelling manually.<\/p>\n<p>It contains <strong>39 hours<\/strong> of open road and various driving scenarios ranging from <strong>urban, highway, suburbs and countryside scenes.<\/strong><\/p>\n<p><strong>Manual bounding box annotations<\/strong> are available for two classes are present: <strong>pedestrians and cars<\/strong>. (Truck and buses are not labelled).<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_row column_structure=&#8221;1_2,1_2&#8243; make_equal=&#8221;on&#8221; _builder_version=&#8221;4.16&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; module_alignment=&#8221;center&#8221; custom_padding=&#8221;0px|||||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;30px||||false|false&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_code _builder_version=&#8221;4.16&#8243; custom_margin=&#8221;||-1px|||&#8221; global_colors_info=&#8221;{}&#8221;]\n\t\t\t<!-- START ATIS Automotive dataset extract REVOLUTION SLIDER 6.7.34 --><p class=\"rs-p-wp-fix\"><\/p>\n\t\t\t<rs-module-wrap id=\"rev_slider_74_3_wrapper\" data-source=\"gallery\" style=\"visibility:hidden;background:transparent;padding:0;margin:0px auto;margin-top:0;margin-bottom:0;\">\n\t\t\t\t<rs-module id=\"rev_slider_74_3\" style=\"\" data-version=\"6.7.34\">\n\t\t\t\t\t<rs-slides style=\"overflow: hidden; position: absolute;\">\n\t\t\t\t\t\t<rs-slide style=\"position: absolute;\" data-key=\"rs-112\" data-title=\"Intro\" data-anim=\"ms:3000;\" data-in=\"o:-0.5;sx:1.6;sy:1.6;e:power3.inOut;\" data-out=\"o:0;sx:0.6;sy:0.6;e:power3.inOut;\">\n\t\t\t\t\t\t\t<img decoding=\"async\" src=\"\/\/www.prophesee-cn.com\/wp-content\/plugins\/revslider\/sr6\/assets\/assets\/dummy.png\" alt=\"\" title=\"Thumbnail-ATIS-Automotive-dataset\" width=\"1864\" height=\"1466\" class=\"rev-slidebg tp-rs-img rs-lazyload\" data-lazyload=\"\/\/www.prophesee-cn.com\/wp-content\/uploads\/2023\/01\/Thumbnail-ATIS-Automotive-dataset.jpg\" data-parallax=\"10\" data-no-retina>\n\t\t\t\t\t\t\t<rs-bgvideo \n\t\t\t\t\t\t\t\tdata-video=\"w:100%;h:100%;ar:4:3;nse:false;l:loopandnoslidestop;ptimer:false;vfc:true;\"\n\t\t\t\t\t\t\t\tdata-mp4=\"\/\/v.prophesee-cn.com\/sv\/33841fb5-185d376fbc4\/33841fb5-185d376fbc4.mp4\"\n\t\t\t\t\t\t\t><\/rs-bgvideo>\n<!--\n\t\t\t\t\t\t\t--><rs-layer\n\t\t\t\t\t\t\t\tid=\"slider-74-slide-112-layer-6\" \n\t\t\t\t\t\t\t\tdata-type=\"image\"\n\t\t\t\t\t\t\t\tdata-rsp_ch=\"on\"\n\t\t\t\t\t\t\t\tdata-xy=\"x:c;y:b,t,t,t;yo:24px,497px,382px,306px;\"\n\t\t\t\t\t\t\t\tdata-text=\"l:22;a:inherit;\"\n\t\t\t\t\t\t\t\tdata-dim=\"w:220px,203px,228px,176px;h:20px,18px,20px,16px;\"\n\t\t\t\t\t\t\t\tdata-frame_999=\"o:0;st:w;sR:8700;\"\n\t\t\t\t\t\t\t\tstyle=\"z-index:7;\"\n\t\t\t\t\t\t\t><img decoding=\"async\" src=\"\/\/www.prophesee-cn.com\/wp-content\/plugins\/revslider\/sr6\/assets\/assets\/dummy.png\" alt=\"\" class=\"tp-rs-img rs-lazyload\" width=\"1985\" height=\"178\" data-lazyload=\"\/\/www.prophesee-cn.com\/wp-content\/uploads\/2023\/01\/Fichier-1@3x.png\" data-no-retina> \n\t\t\t\t\t\t\t<\/rs-layer><!--\n-->\t\t\t\t\t<\/rs-slide>\n\t\t\t\t\t<\/rs-slides>\n\t\t\t\t<\/rs-module>\n\t\t\t\t<script>\n\t\t\t\t\tsetREVStartSize({c: 'rev_slider_74_3',rl:[1240,1024,778,480],el:[700,550,450,350],gw:[1200,600,700,600],gh:[700,550,450,350],type:'hero',justify:'',layout:'fullwidth',mh:\"0\"});if (window.RS_MODULES!==undefined && window.RS_MODULES.modules!==undefined && window.RS_MODULES.modules[\"revslider743\"]!==undefined) {window.RS_MODULES.modules[\"revslider743\"].once = false;window.revapi74 = undefined;if (window.RS_MODULES.checkMinimal!==undefined) window.RS_MODULES.checkMinimal()}\n\t\t\t\t<\/script>\n\t\t\t<\/rs-module-wrap>\n\t\t\t<!-- END REVOLUTION SLIDER -->\n[\/et_pb_code][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.16&#8243; text_font=&#8221;Montserrat||||||||&#8221; text_text_color=&#8221;#1e2534&#8243; custom_padding=&#8221;||0px|||&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3 style=\"text-align: left;\">DATASET CONTENT<\/h3>\n<p>[\/et_pb_text][et_pb_divider color=&#8221;#000000&#8243; divider_weight=&#8221;5px&#8221; _builder_version=&#8221;4.16&#8243; max_width=&#8221;10%&#8221; module_alignment=&#8221;left&#8221; height=&#8221;0px&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_divider][et_pb_text _builder_version=&#8221;4.16&#8243; text_font=&#8221;Montserrat||||||||&#8221; text_text_color=&#8221;#1e2534&#8243; custom_padding=&#8221;||1px|||&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>The dataset is split between train, test and val folders.\u00a0<\/p>\n<p>Files consist of 60 seconds recordings that were cut from longer recording sessions. Cuts from a single recording session are all in the same training split.<\/p>\n<p>Each dat file is a binary file in which events are encoded using 4 bytes (unsigned int32) for the timestamps and 4 bytes (unsigned int32) for the data, encoding is little-endian ordering.<\/p>\n<p>The data is composed of 14 bits for the x position, 14 bits for the y position and 1 bit for the polarity (encoded as -1\/1).<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=&#8221;1_2,1_2&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;||0px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;||30px||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_gallery gallery_ids=&#8221;77742,77744,77746&#8243; fullwidth=&#8221;on&#8221; _builder_version=&#8221;4.19.4&#8243; width=&#8221;75%&#8221; module_alignment=&#8221;center&#8221; auto=&#8221;on&#8221; auto_speed=&#8221;3000&#8243; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;][\/et_pb_gallery][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.16&#8243; text_text_color=&#8221;#1e2534&#8243; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>Annotations use the\u00a0<a class=\"external-link\" href=\"https:\/\/numpy.org\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">numpy\u00a0<\/a>format and can simply be loaded form python using numpy\u00a0<code>boxes = np.load(path)<\/code><\/p>\n<p>Boxes have the following fields<\/p>\n<ul>\n<li><strong><em>x<\/em><\/strong> abscissa of the top left corner in pixels<\/li>\n<li><strong><em>y<\/em><\/strong> ordinate of the top left corner in pixels<\/li>\n<li><strong><em>w<\/em><\/strong> width of the boxes in pixel<\/li>\n<li><strong><em>h<\/em><\/strong> height of the boxes in pixel<\/li>\n<li><strong><em>ts<\/em><\/strong> timestamp of the box in the sequence in microseconds<\/li>\n<li><strong><em>class_id 0<\/em><\/strong> for cars and <strong><em>1<\/em><\/strong> for pedestrians<\/li>\n<\/ul>\n<p>Alongside the dataset, we also release sample code in Python to conveniently read the events and annotations: <a href=\"https:\/\/github.com\/prophesee-ai\/prophesee-automotive-dataset-toolbox\" target=\"_blank\" rel=\"noopener noreferrer\"><span color=\"#ff7276\" style=\"color: #407ec9;\">Github repository<\/span><\/a><\/p>\n<p class=\"p1\">For more information on how the dataset was created, <a href=\"https:\/\/arxiv.org\/abs\/2001.08499\" target=\"_blank\" rel=\"noopener noreferrer\">please refer to the paper.<\/a><span style=\"font-size: 14px;\">\u00a0<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.16&#8243; background_color=&#8221;#1e2534&#8243; custom_padding=&#8221;54px|0px|40px|0px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;27px|0px|0px|0px||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text _builder_version=&#8221;4.16&#8243; text_font=&#8221;Montserrat||||||||&#8221; text_text_color=&#8221;#ffffff&#8221; header_text_color=&#8221;#ffffff&#8221; header_3_text_color=&#8221;#ffffff&#8221; custom_padding=&#8221;||0px|||&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3 style=\"text-align: left;\">CITING PROPHESEE GEN1 AUTOMOTIVE DETECTION DATASET<\/h3>\n<p>[\/et_pb_text][et_pb_divider color=&#8221;#ffffff&#8221; divider_weight=&#8221;5px&#8221; _builder_version=&#8221;4.16&#8243; max_width=&#8221;10%&#8221; module_alignment=&#8221;left&#8221; height=&#8221;0px&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_divider][et_pb_text _builder_version=&#8221;4.16&#8243; text_font=&#8221;||||||||&#8221; text_text_color=&#8221;#ffffff&#8221; text_font_size=&#8221;18px&#8221; inline_fonts=&#8221;Montserrat&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p>When using the data in an academic context, please cite the following paper:<\/p>\n<p>Pierre de Tournemire, Davide Nitti, Etienne Perot and Amos Sironi &#8220;<span>A Large Scale Event-based Detection Dataset for Automotive&#8221;.<\/span><\/p>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2001.08499\" target=\"_blank\" rel=\"noopener noreferrer\">Preprint on ArXiv<\/a><\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; 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