《视频监控外文翻译》由会员分享，可在线阅读，更多相关《视频监控外文翻译（7页珍藏版）》请在金锄头文库上搜索。
1、 京 江 学 院JINGJIANG COLLEGE OF J I A N G S U U N I V E R S I T Y 外 文 文 献 翻 译学生学号： 3081155033 学生姓名： 缪成鹏 专业班级： J电子信息工程0802 指导教师姓名： 李正明 指导教师职称： 教授 2012年6月A System for Remote Video Surveillance and MonitoringThe thrust of CMU research under the DARPA Video Surveillance and Monitoring (VSAM) project is coo
2、perative multi-sensor surveillance to support battlefield awareness. Under our VSAM Integrated Feasibility Demonstration (IFD) contract, we have developed automated video understanding technology that enables a single human operator to monitor activities over a complex area using a distributed netwo
3、rk of active video sensors. The goal is to automatically collect and disseminate real-time information from the battlefield to improve the situational awareness of commanders and staff. Other military and federal law enforcement applications include providing perimeter security for troops, monitorin
4、g peace treaties or refugee movements from unmanned air vehicles, providing security for embassies or airports, and staking out suspected drug or terrorist hide-outs by collecting time-stamped pictures of everyone entering and exiting the building.Automated video surveillance is an important researc
5、h area in the commercial sector as well. Technology has reached a stage where mounting cameras to capture video imagery is cheap, but finding available human resources to sit and watch that imagery is expensive. Surveillance cameras are already prevalent in commercial establishments, with camera out
6、put being recorded to tapes that are either rewritten periodically or stored in video archives. After a crime occurs a store is robbed or a car is stolen investigators can go back after the fact to see what happened, but of course by then it is too late. What is needed is continuous 24-hour monitori
7、ng and analysis of video surveillance data to alert security officers to a burglary in progress, or to a suspicious individual loitering in the parking lot, while options are still open for avoiding the crime.Keeping track of people, vehicles, and their interactions in an urban or battlefield enviro
8、nment is a difficult task. The role of VSAM video understanding technology in achieving this goal is to automatically “parse” people and vehicles from raw video, determine their geolocations, and insert them into dynamic scene visualization. We have developed robust routines for detecting and tracki
9、ng moving objects. Detected objects are classified into semantic categories such as human, human group, car, and truck using shape and color analysis, and these labels are used to improve tracking using temporal consistency constraints. Further classification of human activity, such as walking and r
10、unning, has also been achieved. Geolocations of labeled entities are determined from their image coordinates using either wide-baseline stereo from two or more overlapping camera views, or intersection of viewing rays with a terrain model from monocular views. These computed locations feed into a hi
11、gher level tracking module that tasks multiple sensors with variable pan, tilt and zoom to cooperatively and continuously track an object through the scene. All resulting object hypotheses from all sensors are transmitted as symbolic data packets back to a central operator control unit, where they a
12、re displayed on a graphical user interface to give a broad overview of scene activities. These technologies have been demonstrated through a series of yearly demos, using a testbed system developed on the urban campus of CMU.Detection of moving objects in video streams is known to be a significant,
13、and difficult, research problem. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving blobs provides a focus of attention for recognition, classification, and activity analysis, making these later processes more efficient
14、since only “moving” pixels need be considered.There are three conventional approaches to moving object detection: temporal differencing ; background subtraction; and optical flow. Temporal differencing is very adaptive to dynamic environments, but generally does a poor job of extracting all relevant
15、 feature pixels. Background subtraction provides the most complete feature data, but is extremely sensitive to dynamic scene changes due to lighting and extraneous events. Optical flow can be used to detect independently moving objects in the presence of camera motion; however, most optical flow com
16、putation methods are computationally complex, and cannot be applied to full-frame video streams in real-time without specialized hardware.Under the VSAM program, CMU has developed and implemented three methods for moving object detection on the VSAM testbed. The first is a combination of adaptive background subtraction and three-frame differencing . Th
