本文選題：前景檢測　切入點：多目標跟蹤　出處：《西南交通大學》2014年碩士論文　論文類型：學位論文

【摘要】：隨著意外事故、犯罪和恐怖活動的增加,公共安全顯得越來越重要。面對這些突發(fā)事件,智能視頻監(jiān)控系統(tǒng)能夠及時的給出預警信號或報警。與傳統(tǒng)的人工監(jiān)控攝像頭相比,智能監(jiān)控系統(tǒng)能夠節(jié)省大量的人力、物力和財力,并且能夠更加高效的對這些合法的視頻監(jiān)控數(shù)據(jù)實現(xiàn)自動或者半自動的解釋和分析處理。在智能監(jiān)控系統(tǒng)的研究中,視頻前景檢測、多目標跟蹤和異常行為識別研究作為比較新的研究方向,已經(jīng)成為計算機視覺和模式識別領域的研究熱點,它們的研究對于提高智能監(jiān)控系統(tǒng)的性能具有非常重要的意義。 本文通過對視頻前景檢測、多目標跟蹤和異常行為識別領域的算法分析,對智能監(jiān)控系統(tǒng)的開發(fā)進行了深入的研究。主要完成以下幾個方面的工作： 1.歸納總結了前景檢測領域常用的運動目標檢測方法,并對常用的運動前景檢測方法進行介紹,提出一個改進的基于混合高斯模型的運動目標檢測方法,大大提高了以往基于混合高斯模型的前景檢測的魯棒性和準確性,其抗干擾能力顯著增強。 2.在跟蹤階段,針對單固定攝像頭,提出一個稀疏的多目標跟蹤系統(tǒng)框架。該框架重點是將單目標跟蹤很好的TLD算法和關聯(lián)矩陣結合起來,有效解決多目標跟蹤過程的合并遮擋問題。在目標合并處理階段,對合并的目標加窗且引入雙三次插值算法對初始化的目標和所加窗口進行同比例超分辨縮放。該操作能很好地解決大目標的計算復雜度高和小目標的不能正常初始化問題。對于關聯(lián)矩陣的一些特殊情況進行特殊處理。最后在濾波階段,該框架用分數(shù)階卡爾曼算法代替卡爾曼算法進行濾波,不僅能夠降低機動目標的觀測噪聲,還能在間隔跟丟時準確地預測目標的位置。 3.在基于軌跡的異常檢測階段,本文提出一個基于時間分割的多特征表示的軌跡異常檢測方法。首先提出一種新的軌跡特征表示方法,該方法由六個特征空間組成：1)軌跡的方向和長度,2)軌跡的平均位置,3)初始位置、軌跡分割片段的時間長度、分割片段的方向,4)分割片段序列的平均速度序列,5)分割片段序列的平均加速度序列,6)整條軌跡的最大加速度。接著利用監(jiān)督型的支持向量機分類算法來對軌跡特征集進行訓練、檢測。該方法提高了軌跡異常行為的異常檢測率和識別準確度,降低了虛警率。同時,由于不需要對訓練和測試樣本進行縮放處理從而大大提高了該方法的實用價值。
[Abstract]:With the increase of accidents, crime and terrorist activities, public safety becomes more and more important. In the face of these emergencies, intelligent video surveillance system can give early warning signal or alarm in time. Intelligent monitoring system can save a lot of manpower, material resources and financial resources, and can more efficiently interpret and analyze these legitimate video surveillance data automatically or semi-automatically. As a new research direction, video foreground detection, multi-target tracking and abnormal behavior recognition have become the research focus in the field of computer vision and pattern recognition. Their research is very important for improving the performance of intelligent monitoring system. Through the algorithm analysis of video foreground detection, multi-target tracking and abnormal behavior recognition, the development of intelligent monitoring system is deeply studied in this paper. 1. The common moving target detection methods in the field of foreground detection are summarized, and the commonly used motion foreground detection methods are introduced, and an improved moving target detection method based on mixed Gao Si model is proposed. The robustness and accuracy of foreground detection based on mixed Gao Si model are greatly improved, and its anti-jamming ability is greatly enhanced. 2. In the tracking phase, a sparse multi-target tracking system framework is proposed for a single fixed camera, which focuses on combining the TLD algorithm with the correlation matrix. Effectively solve the merge occlusion problem in the multi-target tracking process. The combined target is windowed and the bi-cubic interpolation algorithm is introduced to scale the initialized object and the added window in the same proportion. This operation can solve the problem of high computational complexity of large target and abnormal initial value of small target. To deal with some special cases of incidence matrix. Finally, in the filtering stage, The frame uses fractional order Kalman algorithm instead of Kalman algorithm to filter, which can not only reduce the observation noise of maneuvering target, but also predict the position of target accurately at interval and loss time. 3. In the phase of locus based anomaly detection, this paper presents a method of trajectory anomaly detection based on multi-feature representation based on time division. Firstly, a new trajectory feature representation method is proposed. The method consists of six feature spaces, the direction and length of the trajectory, the average position of the trajectory, the initial position and the time length of the segment. The direction of the segment is 4) the average velocity sequence of the segment sequence is 5) the average acceleration sequence of the segment sequence is 6) the maximum acceleration of the whole trajectory is obtained. Then the supervised support vector machine classification algorithm is used to train the trajectory feature set. The method improves the detection rate and recognition accuracy of trajectory anomaly behavior and reduces the false alarm rate. At the same time, the practical value of the method is greatly improved because it does not need to scale the training and test samples.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN948.6

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本文編號：1650082