{"id":2,"date":"2023-05-01T17:45:09","date_gmt":"2023-05-01T17:45:09","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/f23team7\/?page_id=2"},"modified":"2023-05-06T22:12:07","modified_gmt":"2023-05-06T22:12:07","slug":"sample-page","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/f23team7\/","title":{"rendered":"Introduction"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Multi-Object Tracking<\/h2>\n\n\n\n<p>Multi-Object Tracking <strong>estimates the trajectory of different objects in videos<\/strong>. It is often used in the retail scene to <strong>analyze and understand the behaviors of customers<\/strong> to study buying patterns, combat fraud, and estimate in-store traffic..<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<pre class=\"wp-block-verse\">Our task boils down to <strong>multi-person tracking<\/strong> using <strong>multiple cameras<\/strong> of different views without post-processing so that the algorithm runs in<strong> real-time<\/strong>.<\/pre>\n<\/blockquote>\n<\/div>\n<\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team7\/wp-content\/uploads\/sites\/84\/2023\/05\/Tracking-by-detection-paradigm-Firstly-an-independent-detector-is-applied-to-all-1.png\" alt=\"\" class=\"wp-image-94\" width=\"479\" height=\"190\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team7\/wp-content\/uploads\/sites\/84\/2023\/05\/Tracking-by-detection-paradigm-Firstly-an-independent-detector-is-applied-to-all-1.png 850w, https:\/\/mscvprojects.ri.cmu.edu\/f23team7\/wp-content\/uploads\/sites\/84\/2023\/05\/Tracking-by-detection-paradigm-Firstly-an-independent-detector-is-applied-to-all-1-300x120.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team7\/wp-content\/uploads\/sites\/84\/2023\/05\/Tracking-by-detection-paradigm-Firstly-an-independent-detector-is-applied-to-all-1-768x306.png 768w\" sizes=\"auto, (max-width: 479px) 100vw, 479px\" \/><figcaption class=\"wp-element-caption\">Leal-Taix\u00e9, Laura. (2014). Multiple object tracking with context awareness.<\/figcaption><\/figure>\n<\/div>\n\n\n<p>The standard approach in multi-object tracking is <strong>tracking-by-detection<\/strong>. From video frames, a set of detections are extracted and then these per-frame detections are associated together by assigning the same ID to the detections that contain the same target, thereby building trajectories.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Phase 1: Spring 2023<\/h2>\n\n\n\n<p>We reviewed relevant literature and decided to implement <strong>tracking-by-detection method in top-down view<\/strong> in order to efficiently aggregate multiple views. We trained two multi-view detectors (<strong>MVDet, MVDeTr<\/strong>) on <strong>MMPTrack dataset<\/strong>, and implemented <strong>SORT<\/strong> as our baseline tracking algorithm.  Detected and tracked bounding boxes in top-down view are reprojected onto camera planes for final output.<\/p>\n\n\n\n<p><a href=\"https:\/\/docs.google.com\/presentation\/d\/1G5FYBfwkkjJXd6FvaAdFW2yxQYEYkTGL8I9kqbKKlhQ\/edit?usp=sharing\" target=\"_blank\" rel=\"noreferrer noopener\">Presentation slides<\/a> and <a href=\"https:\/\/docs.google.com\/presentation\/d\/1co6khluvXxlFg1bXJblkz70zt7Tq9B3ey92OGuRkT8A\/edit?usp=sharing\" target=\"_blank\" rel=\"noreferrer noopener\">poster<\/a> are publicly available.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Phase 2: Fall 2023<\/h2>\n\n\n\n<p>We plan to <strong>refine the projected bounding boxes in camera views<\/strong> and <strong>suppress false positives<\/strong> by utilizing off-the-shelf single-view detectors (e.g. YOLO). Moreover, we aim to improve the latency of our pipeline to <strong>achieve real-time performance<\/strong>. Learning-based tracking algorithms (e.g. DeepSORT, StrongSORT) will be explored to <strong>increase the robustness to occlusion<\/strong> and <strong>minimize ID switches<\/strong>.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Multi-Object Tracking Multi-Object Tracking estimates the trajectory of different objects in videos. It is often used in the retail scene to analyze and understand the behaviors of customers to study buying patterns, combat fraud, and estimate in-store traffic.. Our task boils down to multi-person tracking using multiple cameras of different views without post-processing so that &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team7\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Introduction&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Introduction - Multi-Camera Multi-People Tracking<\/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\/f23team7\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Introduction - Multi-Camera Multi-People Tracking\" \/>\n<meta property=\"og:description\" content=\"Multi-Object Tracking Multi-Object Tracking estimates the trajectory of different objects in videos. 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