基于内容的海量监控视频的多层次检索系统

设计和实现了一种基于内容的海量监控视频的多层次检索系统。该系统首先从监控视频中提取关键帧图像,其次利用行人检测、人脸识别及车辆检测等算法将关键帧中的行人图像、人脸图像和车辆图像等感兴趣目标提取出来,然后提取这些图像的颜色、纹理等特征,利用改进的LIRe(Lucene Image Retrieval)建立分布式的特征库,最终形成了多层次的信息数据库。实验表明,该系统具有较高的检索准确率和较快的检索速率,并支持海量监控视频的检索。

Content-Based Multi-Level Retrieval System for Massive Surveillance Video

In this paper we design and realize a content-based multi-level retrieval system for massive surveillance video. Firstly, key frames are selected from the surveillance videos. Secondly, interested targets, which include pedestrians, faces and cars, are segmented from the chosen key frames through human detection, face recognition and vehicle detection correspondingly. Finally, features like color or texture of these object images are utilized to construct a distributed feature library via improved LIRe (Lucene Image Retrieval). In this way, a multi-level database is established. Experiment results show that the proposed system has performed well on both precision and efficiency, as well as supports the retrieval for massive surveillance video.