Automatic object detection for behavioural research using YOLOv8

Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO (‘you only look once’) and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examin...

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Bibliographic Details
Published in:Behavior research methods Vol. 56; no. 7; pp. 7307 - 7330
Main Author: Hermens, Frouke
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2024
Subjects:
Behavioral Research - methods
Behavioral Science and Psychology
Cognitive Psychology
Humans
Original Manuscript
Psychology
Video Recording - methods
Surgical tool tracking
Automatic object detection
YOLO
Behavioural analysis
ISSN:1554-3528, 1554-3528
Online Access:Get full text
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Summary:Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO (‘you only look once’) and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1554-3528
1554-3528
DOI:10.3758/s13428-024-02420-5