自然保护区海量视频数据快速分析与检索

近年来，随着视频监控系统在自然保护区的大量部署，如何有效利用日益增加的海量视频监控数据成为亟待解决的难题.通过基于图像相似度的关键帧提取算法对海量视频数据进行清洗和压缩，同时利用基于深度学习的目标检测算法提取关键帧中的有效视频信息，并提供多种基于内容的视频检索方式，自动对用户提交的检索内容进行分析与处理，从而快速检索出感兴趣的视频.通过对青海湖野生动物视频监控数据进行分析与检索，验证了该系统的有效性.

Rapid Analysis and Retrieval of Massive Video Data in Nature Reserves

In recent years, a large number of video surveillance systems are deployed in the nature reserves, so it has become an urgent problem how to effectively use the increasing mass of video surveillance data. In this study, an efficient algorithm for key frame extraction based on image similarity is used to clean and compress the massive video data. At the same time, an object detection algorithm based on deep learning is used to extract valid video information. In addition, the system provides a variety of content-based video retrieval methods. It automatically analyzes and processes the search contents submitted by the user so as to quickly retrieve the video of interest. This study analyzes and retrieves the video surveillance data of wild animals in Qinghai Lake, which verifies the correctness of the proposed system.