基于視頻監(jiān)控的目標遮擋問題研究
發(fā)布時間：2019-05-23 11:02
【摘要】：基于視頻監(jiān)控的目標跟蹤技術一直是計算機視覺領域的熱點研究問題,在軍事制導、安防建設和智能交通領域有著廣闊的應用前景。智能視頻監(jiān)控系統(tǒng)包括:視頻采集模塊、圖像預處理模塊、目標檢測模塊、目標跟蹤模塊和智能分析模塊,涉及到模式識別、視覺分析等多個領域。在視頻監(jiān)控系統(tǒng)中,由于背景的復雜多變,目標在運動過程中經常會出現(xiàn)部分或全部被遮擋的情況。本文針對目標跟蹤過程中普遍存在的遮擋后目標易跟錯、跟丟的問題展開研究,提出了基于“物體恒存性”的方法解決長時間遮擋后目標容易跟錯和丟失的情況,有效的解決了單目標和多目標跟蹤過程中長時間遮擋問題。主要研究內容包括:(1)在運動目標檢測方面,針對視頻監(jiān)控中的背景不穩(wěn)定的情況,本文采用混合高斯背景建模方法提取前景運動目標,利用開、閉運算等方法對二值化的前景目標進行連通性處理,去除噪聲點,填補細小的孔洞,獲得較為完整的目標圖像。(2)在運動目標跟蹤方面,提取運動目標的多種類型特征,主要包括顏色、邊緣、紋理、直方圖等,并與目標運動軌跡特征相結合,重點解決目標跟蹤過程中被短時間遮擋后,單一特征消失,目標容易跟錯和跟丟的問題。針對長時間遮擋情況,本文提出了基于“物體恒存性”算法,分析遮擋關系,利用目標未遮擋時提取的多種類型的特征,在遮擋物的附近快速的查找消失目標,提高查找的效率。(3)對本文提出的“物體恒存性”算法,利用“Weizmann”、“KTH”、“CAVIAR”標準視頻庫以及自錄的視頻序列進行實驗驗證。針對跟蹤過程中可能出現(xiàn)的光照突變、短時間遮擋、長時間遮擋以及循環(huán)遮擋等情況,給出了實驗結果和分析,表明該算法能夠有效地解決遮擋問題,達到很好的識別跟蹤效果。
[Abstract]:Target tracking technology based on video surveillance has always been a hot research issue in the field of computer vision, and has a broad application prospect in the fields of military guidance, security construction and intelligent transportation. Intelligent video surveillance system includes: video capture module, image preprocessing module, target detection module, target tracking module and intelligent analysis module, involving pattern recognition, visual analysis and other fields. In video surveillance system, due to the complexity and variability of the background, the target is often partially or completely blocked in the process of motion. In this paper, the problem that the target is easy to follow and lose after occlusion is studied in the process of target tracking, and a method based on "object persistence" is proposed to solve the problem that the target is easy to follow and lose after long time occlusion. It effectively solves the problem of long time occlusion in the process of single target and multi-target tracking. The main research contents are as follows: (1) in the aspect of moving target detection, in view of the unstable background in video surveillance, this paper uses the hybrid Gao Si background modeling method to extract the foreground moving target, using the open, Closed operation and other methods are used to deal with binarization foreground targets, remove noise points, fill small holes, and obtain more complete target images. (2) in moving target tracking, various types of features of moving targets are extracted. It mainly includes color, edge, texture, histogram and so on, and combines with the moving trajectory feature of the target, which focuses on solving the problem that the single feature disappears after a short time occlusion in the process of target tracking, and the target is easy to follow up and lose. In order to solve the problem of long time occlusion, this paper proposes an algorithm based on "object persistence", which analyzes the occlusion relationship and makes use of various types of features extracted by the target when it is not occlusive to quickly find the disappeared target near the occlusive object. (3) the "object persistence" algorithm proposed in this paper is verified by "Weizmann", "KTH", "CAVIAR" standard video libraries and self-recorded video sequences. In view of the possible light mutation, short time occlusion, long time occlusion and cyclic occlusion in the tracking process, the experimental results and analysis show that the algorithm can effectively solve the occlusion problem. Achieve a good recognition and tracking effect.
【學位授予單位】：河北工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN948.6;TP391.41
本文編號：2483846
[Abstract]:Target tracking technology based on video surveillance has always been a hot research issue in the field of computer vision, and has a broad application prospect in the fields of military guidance, security construction and intelligent transportation. Intelligent video surveillance system includes: video capture module, image preprocessing module, target detection module, target tracking module and intelligent analysis module, involving pattern recognition, visual analysis and other fields. In video surveillance system, due to the complexity and variability of the background, the target is often partially or completely blocked in the process of motion. In this paper, the problem that the target is easy to follow and lose after occlusion is studied in the process of target tracking, and a method based on "object persistence" is proposed to solve the problem that the target is easy to follow and lose after long time occlusion. It effectively solves the problem of long time occlusion in the process of single target and multi-target tracking. The main research contents are as follows: (1) in the aspect of moving target detection, in view of the unstable background in video surveillance, this paper uses the hybrid Gao Si background modeling method to extract the foreground moving target, using the open, Closed operation and other methods are used to deal with binarization foreground targets, remove noise points, fill small holes, and obtain more complete target images. (2) in moving target tracking, various types of features of moving targets are extracted. It mainly includes color, edge, texture, histogram and so on, and combines with the moving trajectory feature of the target, which focuses on solving the problem that the single feature disappears after a short time occlusion in the process of target tracking, and the target is easy to follow up and lose. In order to solve the problem of long time occlusion, this paper proposes an algorithm based on "object persistence", which analyzes the occlusion relationship and makes use of various types of features extracted by the target when it is not occlusive to quickly find the disappeared target near the occlusive object. (3) the "object persistence" algorithm proposed in this paper is verified by "Weizmann", "KTH", "CAVIAR" standard video libraries and self-recorded video sequences. In view of the possible light mutation, short time occlusion, long time occlusion and cyclic occlusion in the tracking process, the experimental results and analysis show that the algorithm can effectively solve the occlusion problem. Achieve a good recognition and tracking effect.
【學位授予單位】：河北工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN948.6;TP391.41
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