【摘要】：智能視頻監(jiān)控技術的研究屬于近些年來在計算機視覺領域新興的方向。它主要的研究目標是通過計算機視覺技術、圖像視頻處理技術和人工智能技術,對監(jiān)控視頻的內容進行描述、分析和理解,同時根據分析處理所得的結果對監(jiān)控系統進行控制,進而使得視頻監(jiān)控系統能夠滿足人們對于智能化的要求水平。它的主要研究內容包括:監(jiān)控視頻中運動物體的檢測、跟蹤、識別和行為分析等。本文主要的研究內容為智能視頻監(jiān)控中的運動目標檢測提取方法。針對傳統的運動目標檢測諸多方法中經常出現的易受光照變化、復雜背景、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合的運動目標檢測算法。該算法通過混合高斯模型在時間域上進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合的檢測算法得出初始的運動目標輪廓,并且經過后續(xù)的運算處理,得到完善的所需運動物體。該算法不僅能夠很好的適應所處場景中的背景干擾與漸變的光照條件,而且能夠克服傳統算法中對于目標檢測不準確、邊緣檢測不完整、容易產生空洞和重影等問題的發(fā)生。實驗結果顯示該運算方法復雜度相對適中,具有比較好的實時性和魯棒性,對運動物體檢測的精確度較高。運動目標檢測是智能視頻監(jiān)控中的一個重要環(huán)節(jié),而運動目標的陰影檢測又是運動物體檢測的一個重要步驟。對于目標陰影檢測的正確與否將直接影響到對目標物體的檢測結果。通過對各種陰影檢測方法的學習與研究,我們發(fā)現僅僅通過一種特征進行處理并不能準確的檢測出陰影。因此,本文提出了一種混合顏色信息、光學不變性以及紋理特征的目標陰影檢測方法,通過綜合分析三種信息檢測的結果,從而實現對陰影的有效確定。該算法能夠有效地結合各種方法的優(yōu)勢,在實驗中取得了較好的效果和運行效率。
[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.
【學位授予單位】：天津理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.41;TN948.6

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