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  • 标题:Comparison and study of Pedestrian Tracking using Deep SORT and state of the art detectors
  • 本地全文:下载
  • 作者:Farah Jamal Ansari ; Anushka Dhiman ; Aleem Ali
  • 期刊名称:Ilköğretim Online/Elementary Education Online
  • 印刷版ISSN:1305-3515
  • 电子版ISSN:1305-3515
  • 出版年度:2021
  • 卷号:20
  • 期号:5
  • 页码:7848-7859
  • DOI:10.17051/ilkonline.2021.05.889
  • 语种:English
  • 出版社:Öğretmen Eğitimi Akademisi
  • 摘要:Object Tracking is becoming very popular these days in the computer vision field. It is the process of tracking an object across a sequence of frames. Deep Sort is a very fast and powerful tracking algorithm. It has a practical way of approaching multiple object tracking problems. It uses the appearance information to track objects through occlusions and thereby reducing the identity switches. Performance evaluation and comparison have been performed on pedestrian tracking using the Deep Sort algorithm in conjunction with the various state-of-the-art object detectors: YOLO, SSD and FasterRCNN. Criteria for Evaluation, datasets used for evaluation, along with the quantitative results have been described and discussed in this work..
  • 关键词:Pedestrian Tracking;Deep Sort algorithm;Faster R-CNN;SSD;YOLO
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