Efficient Human-In-The-Loop Object Detection using Bi-Directional Deep SORT and Annotation-Free Segment Identification

Koki Madono, Teppei Nakano, Tetsunori Kobayashi, Tetsuji Ogawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

The present study proposes a method for detecting objects with a high recall rate for human-supported video annotation. In recent years, automatic annotation techniques such as object detection and tracking have become more powerful; however, detection and tracking of occluded objects, small objects, and blurred objects are still difficult. In order to annotate such objects, manual annotation is inevitably required. For this reason, we envision a human-supported video annotation framework in which over-detected objects (i.e., false positives) are allowed to minimize oversight (i.e., false negatives) in automatic annotation and then the over-detected objects are removed manually. This study attempts to achieve human-in-the-loop object detection with an emphasis on suppressing the oversight for the former stage of processing in the aforementioned annotation framework: bi-directional deep SORT is proposed to reliably capture missed objects and annotation-free segment identification (AFSID) is proposed to identify video frames in which manual annotation is not required. These methods are reinforced each other, yielding an increase in the detection rate while reducing the burden of human intervention. Experimental comparisons using a pedestrian video dataset demonstrated that bi-directional deep SORT with AFSID was successful in capturing object candidates with a higher recall rate over the existing deep SORT while reducing the cost of manpower compared to manual annotation at regular intervals.

本文言語English
ホスト出版物のタイトル2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1226-1233
ページ数8
ISBN(電子版)9789881476883
出版ステータスPublished - 2020 12月 7
イベント2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
継続期間: 2020 12月 72020 12月 10

出版物シリーズ

名前2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
国/地域New Zealand
CityVirtual, Auckland
Period20/12/720/12/10

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ
  • 信号処理
  • 決定科学(その他)
  • 器械工学

フィンガープリント

「Efficient Human-In-The-Loop Object Detection using Bi-Directional Deep SORT and Annotation-Free Segment Identification」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル