{"id":277,"date":"2022-12-20T18:50:12","date_gmt":"2022-12-20T18:50:12","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/?page_id=277"},"modified":"2022-12-21T00:47:00","modified_gmt":"2022-12-21T00:47:00","slug":"fall-2022","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/fall-2022\/","title":{"rendered":"Fall 2022"},"content":{"rendered":"\n<h1 class=\"wp-block-heading\">Dataset<\/h1>\n\n\n\n<p>We used the\u00a0<strong>TBD Pedestrian Dataset<\/strong>[1] by CMU. It has the following salient features:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Multiple static cameras (3 views) with intrinsic and extrinsic matrix provided for each camera.<\/li><li>Bird\u2019s eye view scene (matching our target domain)<\/li><li>Provides ground human truth trajectory (3D points projected to Z=0 plane)<\/li><li>Each trajectory matched with pedestrian ID across all 3 viewpoints.<\/li><li>Provides Frame level trajectory coordinates<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"749\" height=\"357\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/wp-content\/uploads\/sites\/66\/2022\/12\/image-9.png\" alt=\"\" class=\"wp-image-212\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/wp-content\/uploads\/sites\/66\/2022\/12\/image-9.png 749w, https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/wp-content\/uploads\/sites\/66\/2022\/12\/image-9-300x143.png 300w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><figcaption>TBD Pedestrian Dataset setup \u2013 provides multi-view time-synchronized video feed with camera matrices<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"749\" height=\"419\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/wp-content\/uploads\/sites\/66\/2022\/12\/image-10.png\" alt=\"\" class=\"wp-image-213\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/wp-content\/uploads\/sites\/66\/2022\/12\/image-10.png 749w, https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/wp-content\/uploads\/sites\/66\/2022\/12\/image-10-300x168.png 300w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><figcaption>2D labeled trajectories for each pedestrian in all 3 camera views. Pedestrians are matched and given the same ID across all 3 views.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">References<\/h2>\n\n\n\n<p>[1] Wang, A., Biswas, A., Admoni, H., &amp; Steinfeld, A. (2022). Towards Rich, Portable, and Large-Scale Pedestrian Data Collection. ArXiv, abs\/2203.01974<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Dataset We used the\u00a0TBD Pedestrian Dataset[1] by CMU. It has the following salient features: Multiple static cameras (3 views) with intrinsic and extrinsic matrix provided for each camera. Bird\u2019s eye view scene (matching our target domain) Provides ground human truth trajectory (3D points projected to Z=0 plane) Each trajectory matched with pedestrian ID across all &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/fall-2022\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Fall 2022&#8221;<\/span><\/a><\/p>\n","protected":false},"author":137,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-277","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Fall 2022 - Modeling and Understanding Pedestrian Behavior<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2022team11\/fall-2022\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fall 2022 - Modeling and Understanding Pedestrian Behavior\" \/>\n<meta property=\"og:description\" content=\"Dataset We used the\u00a0TBD Pedestrian Dataset[1] by CMU. It has the following salient features: Multiple static cameras (3 views) with intrinsic and extrinsic matrix provided for each camera. 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