论文标题
使用对象检测的图像条件基于密钥帧的视频摘要
Image Conditioned Keyframe-Based Video Summarization Using Object Detection
论文作者
论文摘要
视频摘要在选择理解视频的关键帧中起着重要作用。传统上，它旨在在视频中找到最具代表性和最多样化的内容（或框架），以供简短摘要。最近，引入了查询条件的视频摘要，该视频摘要考虑了用户查询以了解更多面向用户的摘要及其偏好。但是，文本查询的用户主观性存在障碍，并在用户查询和输入帧之间找到相似性。在这项工作中，（i）将图像作为用户喜好的查询（ii）提出了一个数学模型，以根据损耗函数和汇总方差以及（iii）查询图像和输入视频之间的相似性分数最小化冗余，以获取摘要视频。此外，已经引入了基于对象的查询图像（OQI）数据集，其中包含查询图像。该方法已使用UT Egentric（UTE）数据集进行了验证。提出的模型成功地解决了（i）用户偏好，（ii）认识重要帧并在日常生活视频中选择该密钥帧的问题，并具有不同的照明条件。所提出的方法达到了UTE数据集的平均F1得分为57.06％，并以11.01％的速度优于现有最先进的方法。该过程时间比最近提出的UTE数据集上的视频实验的实际时间快7.81倍，显示了该方法的效率
Video summarization plays an important role in selecting keyframe for understanding a video. Traditionally, it aims to find the most representative and diverse contents (or frames) in a video for short summaries. Recently, query-conditioned video summarization has been introduced, which considers user queries to learn more user-oriented summaries and its preference. However, there are obstacles in text queries for user subjectivity and finding similarity between the user query and input frames. In this work, (i) Image is introduced as a query for user preference (ii) a mathematical model is proposed to minimize redundancy based on the loss function & summary variance and (iii) the similarity score between the query image and input video to obtain the summarized video. Furthermore, the Object-based Query Image (OQI) dataset has been introduced, which contains the query images. The proposed method has been validated using UT Egocentric (UTE) dataset. The proposed model successfully resolved the issues of (i) user preference, (ii) recognize important frames and selecting that keyframe in daily life videos, with different illumination conditions. The proposed method achieved 57.06% average F1-Score for UTE dataset and outperforms the existing state-of-theart by 11.01%. The process time is 7.81 times faster than actual time of video Experiments on a recently proposed UTE dataset show the efficiency of the proposed method
