<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:doc-9KYHUEDH</identifier><date>2024</date><creator>Li, Ming</creator><relation>documents/doc/9/URN_NBN_SI_doc-9KYHUEDH_001.pdf</relation><relation>documents/doc/9/URN_NBN_SI_doc-9KYHUEDH_001.txt</relation><format format_type="issue">11</format><format format_type="volume">48</format><format format_type="type">article</format><format format_type="extent">str. 59-70</format><identifier identifier_type="DOI">10.31449/inf.v48i11.6033</identifier><identifier identifier_type="ISSN">1854-3871</identifier><identifier identifier_type="COBISSID">218387715</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-9KYHUEDH</identifier><language>eng</language><publisher publisher_location="Ljubljana">Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="eng">Deep SORT</subject><subject language_type_id="slv">gneča</subject><subject language_type_id="slv">pešci</subject><subject language_type_id="slv">sledenje</subject><subject language_type_id="slv">strojno učenje</subject><subject language_type_id="slv">umetna inteligenca</subject><subject language_type_id="slv">varnost</subject><subject language_type_id="eng">Vision Transformer</subject><subject language_type_id="eng">YOLOv5 model</subject><title>Method for top view pedestrian flow detection based on small target tracking</title></Record>