【導(dǎo)讀】視頻在生活中隨處可見，如小區(qū)居民樓中、商場中、保護(hù)野生動物、交通管理、超市中都用到了監(jiān)控技術(shù)。但是現(xiàn)在的傳統(tǒng)監(jiān)控技術(shù)已經(jīng)不能滿足。雜、系統(tǒng)靈活性差、設(shè)備費(fèi)用高、維修不便。隨著計(jì)算機(jī)技術(shù)、網(wǎng)絡(luò)技術(shù)、多。媒體技術(shù)和電子技術(shù)的發(fā)展，視頻監(jiān)控已取得了迅猛的發(fā)展。本文主要介紹了無線視頻監(jiān)控系。統(tǒng)的應(yīng)用領(lǐng)域以及在發(fā)展過程中的三個(gè)階段。還介紹了編碼器結(jié)構(gòu)以及。無線傳輸?shù)母拍?、分類和特點(diǎn)。無線視頻監(jiān)控以其直觀、方便、信息內(nèi)容豐富而廣泛應(yīng)用于許。無線視頻監(jiān)控在礦井監(jiān)控、社區(qū)監(jiān)控、車載系統(tǒng)、社區(qū)安。目的是避免各類刑事案件、公共突發(fā)事。件、自然災(zāi)害、社區(qū)偷盜等事故發(fā)生。另外，確保人身與財(cái)產(chǎn)安全，居。電力行業(yè)是近年對顯示設(shè)備需求增長較快的行業(yè)之一。3，鐵路和軌道交通。此對顯示設(shè)備的需求量快速上升。長距離的監(jiān)控轄區(qū)最后采用無線視頻監(jiān)控系統(tǒng)進(jìn)行預(yù)防。發(fā)展，使我國躍身為全球第二大經(jīng)濟(jì)主體。

　　

【正文】 英文文獻(xiàn) ： The thrust of CMU research under the DARPA Video Surveillance and Monitoring (VSAM) project is cooperative multisensor 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 plex area using a distributed work of active video sensors. The goal is to automatically collect and disseminate realtime information from the battlefield to improve the situational awareness of manders and staff. Other military and federal law enforcement applications include providing perimeter security for troops, monitoring peace treaties or refugee movements from unmanned air vehicles, providing security for embassies or airports, and staking out suspected drug or terrorist hideouts by collecting timestamped pictures of everyone entering and exiting the building. Automated video surveillance is an important research area in the mercial 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 mercial establishments, with camera output 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 24hour monitoring 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 environment is a difficult task. The role of VSAM video understanding technology in 長春理工大學(xué)光電 信息學(xué)院畢業(yè)設(shè)計(jì) 23 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 tracking 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 running, has also been achieved. Geolocations of labeled entities are determined from their image coordinates using either widebaseline stereo from two or more overlapping camera views, or intersection of viewing rays with a terrain model from monocular views. These puted locations feed into a higher 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 are 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. 翻譯： 在美國國防部高級研究計(jì)劃局，視頻監(jiān)控系統(tǒng)項(xiàng)目下進(jìn)行的一系列監(jiān)控裝置研究是一項(xiàng)合作性的多層傳感監(jiān)控，用以支持戰(zhàn)場決策。在我們的視頻監(jiān)控綜合可行性示范條約下，我們已經(jīng)研發(fā)出自動視頻解析技術(shù)，使得單個(gè)操作員通過動態(tài)視頻傳感器的分布式網(wǎng)絡(luò)來監(jiān)測一個(gè)復(fù)雜區(qū)域的一系統(tǒng)活動。 我們的目標(biāo)是自動收集和傳播實(shí)時(shí)的戰(zhàn)場信息，以改善戰(zhàn)場指揮人員的戰(zhàn)場環(huán)境意識。在其他軍事和聯(lián)邦執(zhí)法領(lǐng)域的應(yīng)用包括為部隊(duì)提供邊境安防，通過無人駕駛飛機(jī)監(jiān)控和平條約 及難民流動，保證使館和機(jī)場的安全，通過收集建筑物每個(gè)進(jìn)口和出口的印時(shí)圖片識別可疑毒品和恐怖分子藏匿場所。 自動視頻監(jiān)控在商業(yè)領(lǐng)域同樣也是一個(gè)重要的研究課題。隨著科技的發(fā)展，安裝攝像頭捕捉視頻圖像已經(jīng)非常廉價(jià)，但是通過人為監(jiān)視圖像的成本則非常高昂。監(jiān)視攝像頭已經(jīng)在商業(yè)機(jī)構(gòu)中普遍存在，與相機(jī)輸出記錄到磁帶或者定期重寫或者存儲在錄像檔案。在犯罪發(fā)生后 比如商店被竊或汽車被盜后，再查看當(dāng)時(shí)錄像，往往為時(shí)已晚。盡管避免犯罪還有許多其他的選擇，但 長春理工大學(xué)光電 信息學(xué)院畢業(yè)設(shè)計(jì) 24 現(xiàn)在需要的是連續(xù) 24小時(shí)的監(jiān)測和分析數(shù)據(jù)，由視頻監(jiān)控系統(tǒng)提醒保安人員，及時(shí)發(fā)現(xiàn)正在進(jìn)行的盜竊案，或游蕩在停車場的可疑人員。 在城市或戰(zhàn)場環(huán)境中追蹤人員、車輛是一項(xiàng)艱巨的任務(wù)。 VSAM 視頻解析技術(shù)視頻，確定其 geolocations，并插入到動態(tài)場景可視化。我們已經(jīng)開發(fā)強(qiáng)有力的例程為發(fā)現(xiàn)和跟蹤移動的物體。被測物體分為語義類別，如人力，人力組，汽車和卡車使用形狀和顏色分析，這些標(biāo)簽是用來改善跟蹤一致性使用時(shí)間限制。進(jìn)一步分類的人類活動，如散步，跑步，也取得了。 Geolocations標(biāo)記實(shí)體決心從自己的形象坐標(biāo)使用廣泛的基準(zhǔn)立體 聲由兩個(gè)或兩個(gè)以上的重疊相機(jī)的意見，或查看射線相交的地形模型由單眼意見。這些計(jì)算機(jī)的位置飼料進(jìn)入了更高一級的跟蹤模塊，任務(wù)多傳感器變盤，傾斜和縮放，以合作，不斷追蹤的對象，通過現(xiàn)場。所有產(chǎn)生的對象假設(shè)所有傳感器轉(zhuǎn)交作為象征性的數(shù)據(jù)包返回到一個(gè)中央控制單元操作者，他們都顯示在圖形用戶界面提供了廣泛概述了現(xiàn)場活動。這些技術(shù)已證明，通過一系列每年演示，使用的試驗(yàn)系統(tǒng)上發(fā)展起來的城市校園的債務(wù)工具中央結(jié)算系統(tǒng)。