期刊论文

Yuming Xu

英文

Information Sciences

0020-0255

2021

568

265-285

10.1016/j.ins.2021.03.027

Video-based face detection and tracking technology has been widely applied in the fields of video surveillance, safe driving, human–computer interaction, and medical diagnosis. In video sequences, most existing face detection and tracking methods face interference caused by changes in ambient light, changes in human posture, and occlusion. To achieve accurate face tracking in video sequences, in this paper, we propose an efficient face detection and tracking framework for video sequences based

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Video-based face detection and tracking technology has been widely used in video surveillance, safe driving, and medical diagnosis. In video sequences, most existing face detection and tracking methods face interference caused by occlusion, ambient illumination, and changes in human posture. To accurately track human faces in video sequences, we propose an efficient face detection and tracking framework based on deep learning, which includes a SENResNet face detection model and a Regression Network-based Face Tracking (RNFT) model. Firstly, the SENResNet model integrates the Squeeze and Excita...