本文選題：智能監(jiān)控 + 行人檢測(cè)　； 參考：《國(guó)防科學(xué)技術(shù)大學(xué)》2014年碩士論文

【摘要】：傳統(tǒng)的視頻監(jiān)控系統(tǒng)僅具備視頻的采集和存儲(chǔ)功能,過(guò)分依賴攝像頭硬件,而浪費(fèi)了大量的前端計(jì)算資源,并且非結(jié)構(gòu)化視頻數(shù)據(jù)的監(jiān)視工作也需要大量人工成本。行人作為事故發(fā)生的最主要誘因,是監(jiān)控視頻數(shù)據(jù)中最重要的組成部分之一,視頻中的行人檢測(cè)技術(shù)對(duì)監(jiān)控系統(tǒng)的智能化具有重要意義。本文提出了一種監(jiān)控視頻中的快速行人檢測(cè)方法,主要從行人檢測(cè)基本方法、檢測(cè)分類器的離線訓(xùn)練以及視頻中基于運(yùn)動(dòng)先驗(yàn)信息的行人檢測(cè)三個(gè)方面進(jìn)行研究,并基于該方法提出其在智能監(jiān)控系統(tǒng)的一種應(yīng)用,即監(jiān)控視頻中行人信息的結(jié)構(gòu)化存儲(chǔ)與檢索。本文的主要研究?jī)?nèi)容包括以下幾個(gè)方面:(1)綜合分析了目前主流的幾種行人檢測(cè)方法,最終選取基于積分通道特征和級(jí)聯(lián)Ada Boost分類器的方法作為視頻中行人檢測(cè)的算法基礎(chǔ),并通過(guò)大量實(shí)驗(yàn)進(jìn)行分析,評(píng)估了該方法的檢測(cè)性能和可行性,確定了該方法的適用環(huán)境;(2)針對(duì)Ada Boost分類器的特性和負(fù)樣本訓(xùn)練集的多樣性,提出了一種基于權(quán)重退化控制負(fù)樣本采樣的訓(xùn)練方法,加速了行人檢測(cè)分類器的離線訓(xùn)練過(guò)程,并提升了分類器離線訓(xùn)練的精度,為視頻中的行人檢測(cè)打下了堅(jiān)實(shí)的基礎(chǔ);(3)對(duì)傳統(tǒng)Vi Be方法進(jìn)行了改進(jìn),并基于該方法進(jìn)行運(yùn)動(dòng)檢測(cè)。根據(jù)視頻中的運(yùn)動(dòng)先驗(yàn)信息,限定積分通道特征的提取,并對(duì)視頻幀圖像分塊進(jìn)行行人檢測(cè),有效減小了復(fù)雜背景信息的干擾,提升行人目標(biāo)提取速度的同時(shí),也增強(qiáng)了檢測(cè)的精度;(4)提出了行人檢測(cè)技術(shù)在智能監(jiān)控系統(tǒng)中的應(yīng)用,設(shè)計(jì)了監(jiān)控視頻中行人檢測(cè)、結(jié)構(gòu)化存儲(chǔ)與行人檢索的整體框架,對(duì)該項(xiàng)智能監(jiān)控技術(shù)應(yīng)用進(jìn)行了探索性研究。
[Abstract]:The traditional video surveillance system only has the function of video capture and storage, so it relies on the hardware of camera too much, and it wastes a lot of front-end computing resources, and the monitoring of unstructured video data also needs a lot of labor cost. As the main cause of accidents, pedestrian is one of the most important components of video surveillance data. Pedestrian detection technology in video is of great significance to intelligent monitoring system. In this paper, a fast pedestrian detection method in surveillance video is proposed, which is mainly studied from three aspects: the basic method of pedestrian detection, the off-line training of detection classifier and the pedestrian detection based on prior motion information in video. Based on this method, an application of this method in intelligent monitoring system is proposed, that is, structured storage and retrieval of pedestrian information in surveillance video. The main research contents of this paper include the following aspects: 1) synthetically analyzing several popular pedestrian detection methods, finally selecting the method based on integral channel feature and cascaded Ada Boost classifier as the basis of pedestrian detection algorithm in video. Through a large number of experiments, the detection performance and feasibility of the method are evaluated, and the suitable environment for this method is determined. (2) aiming at the characteristics of the Ada Boost classifier and the diversity of the negative sample training set, A training method based on weight degradation control negative sample sampling is proposed, which accelerates the off-line training process of pedestrian detection classifier and improves the accuracy of off-line training. A solid foundation is laid for pedestrian detection in video. (3) the traditional Vi be method is improved, and the motion detection is carried out based on this method. According to the prior information of motion in video, the feature extraction of integral channel is limited, and the pedestrian detection of video frame image is carried out, which effectively reduces the interference of complex background information and improves the speed of pedestrian target extraction at the same time. The application of pedestrian detection technology in intelligent monitoring system is proposed, and the overall framework of pedestrian detection, structured storage and pedestrian retrieval in surveillance video is designed. The application of this intelligent monitoring technology is studied in this paper.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)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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本文編號(hào)：1929147