Video Scene Information Detection Based on Entity Recognition. (31st October 2021)

• Record Type: Journal Article
• Authors: Qian, Hui; Dai, Mengxuan; Ma, Yong; Zhao, Jiale; Liu, Qinghua; Tao, Tao; Yin, Shugang; Li, Haipeng; Zhang, Youcheng
• Other Names: Chen Chi-Hua Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-10-31
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/1020044
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Video situational information detection is widely used in the fields of video query, character anomaly detection, surveillance analysis, and so on. However, most of the existing researches pay much attention to the subject or video backgrounds, but little attention to the recognition of situational information. What is more, because there is no strong relation between the pixel information and the scene information of video data, it is difficult for computers to obtain corresponding high-level scene information through the low-level pixel information of video data. Video scene information detection is mainly to detect and analyze the multiple features in the video and mark the scenes in the video. It is aimed at automatically extracting video scene information from all kinds of original video data and realizing the recognition of scene information through "comprehensive consideration of pixel information and spatiotemporal continuity." In order to solve the problem of transforming pixel information into scene information, this paper proposes a video scene information detection method based on entity recognition. This model integrates the spatiotemporal relationship between the video subject and object on the basis of entity recognition, so as to realize the recognition of scene information by establishing mapping relation. The effectiveness and accuracy of the model are verified by simulation experiments with the TV series as experimental data. The accuracy of this model in the simulation experiment can reach more than 85%.