Video object detection via space–time feature aggregation and result reuse

Autor: Liang Duan, Rongfei Yang, Kun Yue, Zhengbao Sun, Guowu Yuan
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Image Processing, Vol 18, Iss 12, Pp 3356-3367 (2024)
Druh dokumentu: article
ISSN: 1751-9667
1751-9659
DOI: 10.1049/ipr2.13179
Popis: Abstract When detecting the objects in videos, motion always leads to object deterioration, like blurring and occlusion, as well as the strange state of the object's shape and posture. Consequently, the detection of video frames will lead to a decline in accuracy by using the image object detection model. This paper proposes an online video object detection method based on the one‐stage detector YOLOx. First, the module for space–time feature aggregation is given, which uses the space–time information of past frames to enhance the feature quality of the current frame. Then, the module for result reuse is given, which incorporates the detection results of past frames to improve the detection stability of the current frame. By these two modules, the trade‐off between accuracy and speed of video object detection could be achieved. Experimental results on the ImageNet VID show the improvement of speed and accuracy of the proposed method.
Databáze: Directory of Open Access Journals
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