【正文】 煙霧區(qū)域檢測階段中，使用每個 DCT 塊的 DC值 的 44 系數(shù)的幾個連續(xù)幀 對煙霧的 動作和顏色屬性分析 來確定煙霧區(qū)域 實驗著火點 。 在提出的方案中 ， 幾個煙特性 ， 比如運動、顏色和膨脹特性直接在 DCT 域進行了分析 ， 避免高耗時的逆離散馀弦轉(zhuǎn)換操作。 therefore this feature has been used monly in several smoke detection algorithms [8,11]. Almost all algorithms used Chen’s smoke color model [1],in which the smoke color is determined using RGB color spacebased rules. The first rule is based other fact that the smoke color is gray, which means intensities of three colorchannels are approximately the same. The second rule determines that the gray intensity must be between 80 and 220. This range indicates that the smoke color is neither so white nor so black. 3 Conclusions In this paper we have proposed an early fire detection scheme using Inter Protocol (IP) camera technology with Motion JPEG (MJPEG) codec, in which the Discrete Cosine Transform (DCT) coefficients of each block of size 8 8 are available as input data. In the proposed scheme, several smoke features, such as motion, color and expansion properties are analyzed directly in the DCT domain, avoiding high timeconsuming inverse DCT operations. To increase the accuracy of smoke property estimation, the DCT Intertransformation [14,15] is introduced as a preprocessing operation, which allows changing the block size from 8 8 to 4 4 without inverse DCT. The proposed scheme is evaluated using 50 video sequences with smoke and other 50 video sequences without 12 smoke, obtaining false positive error rates of about 2% and false negative error rates approximately equal to 4%. The principal reason for the false negative errors occurring in two video sequences with smoke is the great similarity of color between the background and smoke。擬議計劃 的 50 個視頻序列沒有硝煙的煙霧和其他 50 個視頻序列評估，取得假陽性錯誤率約 2％ ， 假陰性錯誤率約等于 4％。 通過 使用連接元件標記法 排除 非煙霧區(qū)域的候選區(qū)域的 煙的 膨脹性能分析。11 [13]。 文獻 [9]中的算法目的 是檢測煙和火焰內(nèi)的隧道，火災(zāi)探測是基于使用的背景圖像提取出的運動區(qū) 域，并 分析 運動歷史圖像以及不變矩。該功能的描述取決于燃燒對象產(chǎn)生煙的化學性質(zhì)、火溫度、氧的含量等等。接下來使用形態(tài)學操作降低噪聲， 最后 確定實驗 煙區(qū)的生長特性 。因此對早期火災(zāi)探測， 感煙探測方案可能更有效。 然而為了有效地使用 IP技術(shù)的火災(zāi)探測 ， 本的煙檢測算法必須直接在離散余弦變換（ DCT）域 執(zhí)行 ，因為解碼（從 DCT域空間域）和可能的編碼（從空間 DCT域的域）是相當高的耗時過程。 該框圖 由四個階段組成：視頻幀采集階段 、基于 DCT 變換間預(yù)處理階段 、 煙霧區(qū)域檢測階段和區(qū)域的分析階段。這個范圍表明煙霧的顏色既不是白色的也不這么黑 的 。 therefore the presence of smoke is an essential factor for early fire detection. The features that describe the smoke depend on chemical properties of the busting object, the fire temperature, the amount of oxygen, and so on. Generally the smoke color range goes from white to whitebluish when the 7 bustion temperature is low, and from gray to black when the temperature rises to ignition. The most mon smoke detectors are based on infrared or ultraviolet cameras, while other detection techniques are based on the analysis of particles, temperature, relative humidity and air transparency. Those systems are activated until the smoke particles or flames are very close to the fire detector device, moreover those devices cannot provide more information regarding to the exact location of fire, magnitude, growth rate and so on [1]. To provide more accurate and reliable smoke detection, some video processingbased detection systems have been proposed. Generally the video processingbased fire detection algorithms are carried out using two principal characteristics of fire, which are flame and smoke. Almost all fire detection algorithms in the literature perform a pixel level analysis using some flame and/or smoke properties, such as the flame/