关键词: 视频图像, 目标标注, 目标检测, 目标跟踪, 关键帧提取

Abstract:

Aiming at the problem that the target between consecutive frames is redundant in the video and manual annotation is time-consuming and laborious, a semi-automatic video target annotation framework by combining detection and tracking is proposed. First, manually annotated samples are used to train the improved YOLOv3 detection model offline and the detection model is used as an online annotation detector. Then during online annotation, the target position and label are determined manually in the first frame, target position is determined automatically according to the IOU（Intersection-Over-Union） of the detection box and the tracking box in the subsequent frame, and the response of the tracker is used to judge the target disappearance so that the current target annotation is stopped automatically. Finally, a key frame extraction algorithm based on the target saliency is used to select the key frames. The performance comparison experiment of the improved YOLOv3 is carried out by using the self-built ship target data set, and the effectiveness of the semi-automatic video target annotation method is verified by using a ship video sequence. Experimental results show that this method can improve the annotation efficiency and generate annotated data quickly, and it is suitable for video target annotation tasks in scenes such as sea-surface ships video.

Key words: video image, target annotation, target detection, target tracking, key frames extraction