【摘要】：智能視頻監(jiān)控系統(tǒng)能夠自動(dòng)地監(jiān)控場(chǎng)景,當(dāng)發(fā)現(xiàn)場(chǎng)景中有違規(guī)行為時(shí)立即引發(fā)警報(bào),從而大大減小了工作人員的工作量,提高了檢測(cè)的準(zhǔn)確率,所以智能視頻監(jiān)控系統(tǒng)與傳統(tǒng)的視頻監(jiān)控系統(tǒng)相比更適用于安保工作。目前放置遺留物品是恐怖襲擊的主要手段之一,尤其在那些人口流動(dòng)比較大的公共場(chǎng)所遺留物體的檢測(cè)問題更不能被忽略。對(duì)于人口密集的公共場(chǎng)所和一些安全級(jí)別較高的部門進(jìn)行實(shí)時(shí)的、全天候的遺留物體檢測(cè)就變得特別重要。本文研究的主要內(nèi)容是基于視頻的遺留物檢測(cè)算法,其主要任務(wù)是對(duì)監(jiān)控場(chǎng)景進(jìn)行遺留物判別和放置者檢測(cè)。遺留物檢測(cè)算法主要分為兩大類分別是基于跟蹤的方法和基于目標(biāo)檢測(cè)的方法,本論文中采用的是基于目標(biāo)檢測(cè)的遺留物檢測(cè)算法。在本文中對(duì)原算法做出了兩個(gè)方面的改進(jìn)。一方面,為了降低計(jì)算的復(fù)雜度,把算法的前景提取算法改成了混合差分法;另一方面在原算法中加入了對(duì)部分靜止物體的判斷。具體來說,本文遺留物檢測(cè)算法使用的是基于靜止前景物體減法的方法,該算法主要分為背景維護(hù)、遺留物體判別、放置者檢測(cè)三個(gè)階段。在背景維護(hù)階段,采用混合差分法建立背景模型,并且得到前景圖像,使用這種方法既可以得到運(yùn)動(dòng)前景物體也可以得到靜止的前景物體;在遺留物體判別階段,為前景建立三維模型把前景物體分為運(yùn)動(dòng)前景物體和遺留物體�；诨旌喜罘址ê腿S建模的遺留物檢測(cè)算法對(duì)于移走物體所引起的“鬼影”、物體的部分靜止和遮擋等都有較高的魯棒性。在遺留物放置者檢測(cè)階段使用的是視頻回溯法,找出遺留物體剛進(jìn)入該區(qū)域和完全進(jìn)入該區(qū)域的一段視頻數(shù)據(jù),計(jì)算出其所有圖像中候選放置者所在區(qū)域的顏色直方圖,然后計(jì)算其的平均值,把直方圖最接近平均值的那幀圖像中的候選放置者視為真正的放置者。此外,詳述了實(shí)驗(yàn)的運(yùn)行環(huán)境和系統(tǒng)的整體結(jié)構(gòu),設(shè)定了算法中的主要參數(shù)值,用不同標(biāo)準(zhǔn)視頻庫中的數(shù)據(jù)對(duì)該算法進(jìn)行功能測(cè)試和性能評(píng)估。實(shí)驗(yàn)結(jié)果表明該算法能夠較好的完成遺留物檢測(cè)和放置者檢測(cè)的任務(wù),并且有較好的魯棒性和準(zhǔn)確性。最后,運(yùn)用該遺留物檢測(cè)算法設(shè)計(jì)了一個(gè)遺留物體檢測(cè)系統(tǒng)。
[Abstract]:The intelligent video surveillance system can automatically monitor the scene, and when there are violations in the scene, the alarm can be triggered immediately, which greatly reduces the workload of the staff and improves the accuracy of detection. So the intelligent video surveillance system is more suitable for security than the traditional video surveillance system. At present, the placement of residual objects is one of the main methods of terrorist attacks, especially in those public places where population mobility is relatively large, the detection of residual objects should not be ignored. Real-time, round-the-clock detection of legacy objects is particularly important for densely populated public places and some higher-security departments. The main content of this paper is the video based residue detection algorithm, the main task of which is to detect the residue and the placer of the monitoring scene. Legacy detection algorithms are divided into two main categories, one is based on tracking method and the other is based on target detection. In this paper, the algorithm based on target detection is used. In this paper, the original algorithm is improved in two aspects. On the one hand, in order to reduce the computational complexity, the foreground extraction algorithm is changed into the hybrid difference method; on the other hand, the judgment of some static objects is added to the original algorithm. Specifically, this algorithm is based on static foreground object subtraction, which is divided into three stages: background maintenance, residual object discrimination, and placement detection. In the stage of background maintenance, the background model is established by using the mixed difference method, and the foreground image is obtained. By using this method, the moving foreground object can be obtained as well as the stationary foreground object. In the phase of discrimination, the foreground objects are divided into moving foreground objects and leftover objects. The hybrid difference method and 3D modeling based detection algorithm are robust to the "ghost" caused by the removal of objects, the partial stillness and occlusion of objects. A video traceback method is used in the detection phase of the remnant Placer to find out a segment of video data that has just entered the region and enter the region completely, and to calculate the color histogram of the region in which the candidate Placer is located in all of its images. Then the average value is calculated and the candidate Placer in the frame of the histogram closest to the average is regarded as the true Placer. In addition, the running environment of the experiment and the whole structure of the system are described in detail, the main parameters of the algorithm are set up, and the function and performance of the algorithm are tested and evaluated with the data from different standard video libraries. The experimental results show that the proposed algorithm can accomplish the task of detecting the remnants and the placer, and has good robustness and accuracy. Finally, a legacy object detection system is designed by using the algorithm.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.41;TN948.6

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