【摘要】：智能視頻監(jiān)控技術(shù)的研究屬于近些年來在計(jì)算機(jī)視覺領(lǐng)域新興的方向。它主要的研究目標(biāo)是通過計(jì)算機(jī)視覺技術(shù)、圖像視頻處理技術(shù)和人工智能技術(shù),對監(jiān)控視頻的內(nèi)容進(jìn)行描述、分析和理解,同時(shí)根據(jù)分析處理所得的結(jié)果對監(jiān)控系統(tǒng)進(jìn)行控制,進(jìn)而使得視頻監(jiān)控系統(tǒng)能夠滿足人們對于智能化的要求水平。它的主要研究內(nèi)容包括:監(jiān)控視頻中運(yùn)動(dòng)物體的檢測、跟蹤、識(shí)別和行為分析等。本文主要的研究內(nèi)容為智能視頻監(jiān)控中的運(yùn)動(dòng)目標(biāo)檢測提取方法。針對傳統(tǒng)的運(yùn)動(dòng)目標(biāo)檢測諸多方法中經(jīng)常出現(xiàn)的易受光照變化、復(fù)雜背景、陰影等因素影響的問題,提出了一種由混合高斯模型、邊緣檢測法與連續(xù)幀間差分法三種算法相結(jié)合的運(yùn)動(dòng)目標(biāo)檢測算法。該算法通過混合高斯模型在時(shí)間域上進(jìn)行背景的建模與更新,在空間域上利用由邊緣檢測算法、連續(xù)幀間差分法以及混合高斯模型相結(jié)合的檢測算法得出初始的運(yùn)動(dòng)目標(biāo)輪廓,并且經(jīng)過后續(xù)的運(yùn)算處理,得到完善的所需運(yùn)動(dòng)物體。該算法不僅能夠很好的適應(yīng)所處場景中的背景干擾與漸變的光照條件,而且能夠克服傳統(tǒng)算法中對于目標(biāo)檢測不準(zhǔn)確、邊緣檢測不完整、容易產(chǎn)生空洞和重影等問題的發(fā)生。實(shí)驗(yàn)結(jié)果顯示該運(yùn)算方法復(fù)雜度相對適中,具有比較好的實(shí)時(shí)性和魯棒性,對運(yùn)動(dòng)物體檢測的精確度較高。運(yùn)動(dòng)目標(biāo)檢測是智能視頻監(jiān)控中的一個(gè)重要環(huán)節(jié),而運(yùn)動(dòng)目標(biāo)的陰影檢測又是運(yùn)動(dòng)物體檢測的一個(gè)重要步驟。對于目標(biāo)陰影檢測的正確與否將直接影響到對目標(biāo)物體的檢測結(jié)果。通過對各種陰影檢測方法的學(xué)習(xí)與研究,我們發(fā)現(xiàn)僅僅通過一種特征進(jìn)行處理并不能準(zhǔn)確的檢測出陰影。因此,本文提出了一種混合顏色信息、光學(xué)不變性以及紋理特征的目標(biāo)陰影檢測方法,通過綜合分析三種信息檢測的結(jié)果,從而實(shí)現(xiàn)對陰影的有效確定。該算法能夠有效地結(jié)合各種方法的優(yōu)勢,在實(shí)驗(yàn)中取得了較好的效果和運(yùn)行效率。
[Abstract]:The research of intelligent video surveillance technology is a new direction in the field of computer vision in recent years. Its main research goal is to describe, analyze and understand the content of surveillance video through computer vision technology, image and video processing technology and artificial intelligence technology, and control the monitoring system according to the result of analysis and processing. So that the video surveillance system can meet the requirements of people for the level of intelligence. Its main research contents include: detection, tracking, recognition and behavior analysis of moving objects in surveillance video. The main research content of this paper is the method of moving target detection in intelligent video surveillance. A mixed Gao Si model is proposed to solve the problems which are often affected by the changes of illumination, complex background, shadow and so on in the traditional methods of moving target detection. A moving target detection algorithm based on edge detection and continuous frame difference. The algorithm uses the hybrid Gao Si model to model and update the background in the time domain, and uses the edge detection algorithm, the continuous inter-frame difference method and the mixed Gao Si model to obtain the initial moving target contour in the spatial domain, which is composed of the edge detection algorithm, the continuous inter-frame difference method and the mixed Gao Si model. And after the subsequent processing, we can get the perfect moving object. This algorithm can not only adapt to the background interference and the gradual illumination condition in the scene, but also overcome the problems of inaccurate target detection and incomplete edge detection in traditional algorithms. The experimental results show that the algorithm is relatively moderate in complexity, real-time and robust, and has high accuracy for moving object detection. Moving target detection is an important part of intelligent video surveillance, and shadow detection of moving object is an important step in moving object detection. Whether the target shadow detection is correct or not will directly affect the target object detection results. Through the study of various shadow detection methods, we find that only one feature processing can not accurately detect shadow. Therefore, in this paper, a method of shadow detection based on mixed color information, optical invariance and texture features is proposed. By synthetically analyzing the results of three kinds of information detection, the shadow can be effectively determined. The algorithm can effectively combine the advantages of various methods and achieve good results and operational efficiency in the experiment.
【學(xué)位授予單位】：天津理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.41;TN948.6

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