目的 随着视频监控技术的日益成熟和监控设备的普及,视频监控应用日益广泛,监控视频数据量呈现出爆炸性的增长,已经成为大数据时代的重要数据对象。然而由于视频数据本身的非结构化特性,使得监控视频数据的处理和分析相对困难。面对大量摄像头采集的监控视频大数据,如何有效地按照视频的内容和特性去传输、存储、分析和识别这些数据,已经成为一种迫切的需求。方法 本文面向智能视频监控中大规模视觉感知与智能处理问题,围绕监控视频编码、目标检测与跟踪、监控视频增强、视频运动与异常行为识别等4个主要研究方向,系统阐述2013年度的技术发展状况,并对未来的发展趋势进行展望。结果 中国最新制定的国家标准AVS2在对监控视频的编码效率上比最新国际标准H.265/HEVC高出一倍,标志着我国的视频编码技术和标准在视频监控领域已经实现跨越;视频运动目标检测跟踪的研究主要集中在有效特征提取和分类器训练等方面,机器学习等方法的引入,使得基于多实例学习、稀疏表示的运动目标检测跟踪成为研究的热点;监控视频质量增强主要包括去雾、去夜色、去雨雪、去模糊和超分辨率增强等多方面的内容,现有的算法均是对某类图像清晰化效果较好,而对其他类则相对较差,普适性不高;现有的智能动作分析与异常行为识别技术虽然得到了不断发展,算法的性能也在不断提高,但是从实用角度,除了简单的特定或可控场景外,还没有太多成熟的应用系统。结论 随着大数据时代的到来,智能视频监控的需求将日益迫切,面对众多挑战的同时,该研究领域将迎来前所未有的重大机遇,必将产生越来越多可以实用的研究成果。

Objective With the increasing maturity of video surveillance technologies and popularity of surveillance equipment, video surveillance applications are increasingly widespread. The amounts of surveillance video are showing the explosive growth. In the era of big data, the data of surveillance video has become one of the important data objects. However, due to the unstructured nature of video data, the processing and analysis of multimedia data is relatively difficult. In face of huge video data captured by a large number of surveillance cameras, how to effectively transmit, store, analyze and identify in accordance with the multimedia content and features, has become an urgent need. Method For the problems of large scale visual perception and intelligent processing in the area of intelligent video surveillance, this report is organized around surveillance video encoding, target detection and tracking, augmented surveillance video together with video motion and identifying abnormal behavior four research directions, and elaborate their development status in 2013 and future development trend outlook. Result China's latest national standards AVS2 has twice coding efficiency of surveillance video than the latest international standard H.265/HEVC, which marks the video coding technology and standard of our country has already realized the leap in the field of video surveillance. Study on the detection and tracking of video moving object is focused on effective feature extraction and classifier training, and the introduction of machine learning method makes the moving target detection and tracking based on multiple instance learning and sparse representation become a hot spot of research. Surveillance video quality enhancement includes removing of fog, rain, snow, and night, deblurring and super-resolution enhancement, whose existing algorithms are good for a certain class of image clear effect, and on the other class is relatively poor, universality is not high. Intelligent action analysis and abnormal behavior recognition technology existing got continuous development, the performance of the algorithm is also rising, but from a practical point of view, in addition to a specific scene or controllable simple, there is not yet too many mature application system. Conclusion With the arrival of the era of big data, intelligent video surveillance needs to be increasingly urgent, in the face of many challenges at the same time, this research field will welcome great opportunity hitherto unknown, will have more and more research to practical.