智能视频监控与检索系统开发

发布时间：2018-05-18 12:18

  本文选题：智能视频监控 + 智能存储　； 参考：《南京理工大学》2016年硕士论文

【摘要】：随着信息技术的快速发展,智能视频监控和检索技术得到了广泛的关注和研究。与传统视频监控相比,智能视频监控和检索系统涉及机器视觉、模式识别、人工智能等多个学科研究领域,利用智能算法使计算机协助人完成监控和检索的工作。本文主要设计完成的工作为：1、智能视频监控存储系统；2、监控视频智能检索系统,该部分又分成目标检测、目标跟踪、目标检索三个子系统。文章最后设计实现了智能视频监控和检索系统。传统视频监控在连续录像存储过程中,存在冗余信息,既消耗大量存储空间,又使后期信息检索效率下降。本文研究完成了一种智能视频监控存储方法,依据是否存在运动物体,来对视频信息进行选择性存储,在节省存储空间的同时也为后续查找和检索提供便利。设计完成的监控视频智能检索系统,通过提取监控视频中的运动物体来建立检索库,并根据用户需求内容进行检索。主要工作为：1、改进的Vibe目标检测算法。首先通过根据前景、鬼影和各自邻近区域直方图相似度比较抑制鬼影；其次利用阴影亮度比背景区域低的特性去除阴影；最后根据形态学算法填补空洞。实验表明相较于混合高斯建模、传统Vibe算法,本文改进Vibe算法检测F1-measure分别提高了24%和5%,且实时性较好。2、分块多特征自适应融合的多目标跟踪算法。采用目标底层颜色、纹理和边缘多特征自适应融合,对目标和模板目标分块特征匹配,结合Kalman预测能够鲁棒跟踪遮挡目标,实验表明对遮挡目标识别率为95.3%,每帧图像平均处理时间为36.2ms。3、基于内容和语义的检索方法。结合颜色直方图和SIFT特征进行样例检索,检索结果平均查全率为86%,平均查准率为88%,平均检索时间为13s；通过SVM对图像分类,优化参数,语义检索识别率为87%。最后,本文设计实现了智能视频监控和检索系统。本系统在智能视频监控存储上节省率为30%,在目标检测和目标跟踪满足实时性的要求,在目标检索上查准率可以达到86%以上,该系统能够达到较满意的效果。
[Abstract]:With the rapid development of information technology, intelligent video surveillance and retrieval technology has received extensive attention and research. Compared with traditional video surveillance, intelligent video surveillance and retrieval system involves many subjects such as machine vision, pattern recognition, artificial intelligence and so on. The main work of this paper is designed and completed as follows: 1, the intelligent video surveillance storage system, the intelligent video retrieval system, which is divided into three subsystems: target detection, target tracking and target retrieval. Finally, an intelligent video surveillance and retrieval system is designed and implemented. In the process of continuous video storage, the traditional video surveillance has redundant information, which not only consumes a lot of storage space, but also reduces the efficiency of information retrieval in the later stage. In this paper, an intelligent video surveillance storage method is developed, which can selectively store video information according to the existence of moving objects. It can save storage space and provide convenience for subsequent search and retrieval. The intelligent retrieval system of surveillance video is designed and completed. By extracting the moving objects from the surveillance video, the retrieval database is established, and the retrieval is carried out according to the content of the users. The main work is 1: 1, an improved Vibe target detection algorithm. Firstly, the shadow is suppressed by comparing the similarity between the ghost and their adjacent region histogram according to the foreground; secondly, the shadow is removed by using the feature that the shadow brightness is lower than that of the background region; finally, the void is filled according to the morphological algorithm. The experimental results show that compared with hybrid Gao Si modeling and traditional Vibe algorithm, the improved Vibe algorithm improves the detection F1-measure by 24% and 5% respectively, and has a better real-time performance of .2. a multi-target tracking algorithm based on block multi-feature adaptive fusion is proposed in this paper. Adaptive fusion of bottom color, texture and edge features is used to match the block feature of target and template object. Combined with Kalman prediction, the occlusion target can be tracked robustly. Experiments show that the recognition rate of occluded objects is 95.3 and the average processing time per frame is 36.2ms.3. the retrieval method based on content and semantics is presented. Combining color histogram with SIFT features, the retrieval results show that the average recall rate is 86, the average precision is 88, the average retrieval time is 13 s, and the recognition rate of semantic retrieval is 87 by SVM to classify the image and optimize the parameters. Finally, this paper designs and implements an intelligent video surveillance and retrieval system. The system can save 30% in intelligent video surveillance storage, meet the real-time requirements in target detection and target tracking, and achieve more than 86% precision in target retrieval. The system can achieve satisfactory results.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41;TN948.6
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本文编号：1905806