{"id":6731,"date":"2025-09-10T07:03:38","date_gmt":"2025-09-10T07:03:38","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/09\/10\/hungarian-algorithm-and-its-applications-in-computer-vision\/"},"modified":"2025-09-10T07:03:38","modified_gmt":"2025-09-10T07:03:38","slug":"hungarian-algorithm-and-its-applications-in-computer-vision","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/09\/10\/hungarian-algorithm-and-its-applications-in-computer-vision\/","title":{"rendered":"The Hungarian Algorithm and Its Applications in Computer\u00a0Vision"},"content":{"rendered":"<p>    The Hungarian Algorithm and Its Applications in Computer\u00a0Vision<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>Introduction Multi-object tracking (MOT) is a task in which an algorithm must detect and track multiple objects in a video. Most known algorithms are based on using simple detectors (e.g. YOLO) designed for processing individual images. The overall method involves separately using a detector on consecutive video frames and then matching the corresponding bounding boxes [\u2026]<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/hungarian-algorithm-and-its-applications-in-computer-vision\/\">The Hungarian Algorithm and Its Applications in Computer\u00a0Vision<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Vyacheslav Efimov<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/hungarian-algorithm-and-its-applications-in-computer-vision\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Hungarian Algorithm and Its Applications in Computer\u00a0Vision Introduction Multi-object tracking (MOT) is a task in which an algorithm must detect and track multiple objects in a video. Most known algorithms are based on using simple detectors (e.g. YOLO) designed for processing individual images. The overall method involves separately using a detector on consecutive video [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,69,221,240,3742,70,3743],"tags":[778,3744,269],"class_list":["post-6731","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-artificial-intelligence","category-computer-vision","category-editors-pick","category-hungarian-algorithm","category-machine-learning","category-object-tracking","tag-algorithm","tag-hungarian","tag-its"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6731"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=6731"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6731\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=6731"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=6731"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=6731"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}