發(fā)布時間：2018-08-21 08:37

【摘要】：近年來,隨著科技的發(fā)展和社會的進步,計算機視覺技術(shù)的應(yīng)用已越來越多地呈現(xiàn)在人們的生活當(dāng)中,其中有大部分的應(yīng)用都是基于視頻監(jiān)控的應(yīng)用,學(xué)校、醫(yī)院、車站等公共場所都有大量的監(jiān)控攝像頭,為人們的社會生活安全提供了保障,此外交通監(jiān)控也變得越來越智能化,公路、鐵路等部署的智能探頭已經(jīng)能夠自動解決很多事宜,并能記錄關(guān)鍵的事件,上述智能監(jiān)控應(yīng)用的技術(shù)實現(xiàn)多數(shù)依靠計算機視覺理論,而計算機視覺分支之一目標跟蹤也成為近年的研究熱點,但是真實場景中,目標跟蹤面臨很多困難,具有遮擋、目標多尺度變化、光照變化等阻礙因素,而本文工作的目的就是在前人的工作基礎(chǔ)之上進行研究,主要解決目標跟蹤過程中的遮擋和尺度變化兩個問題,具體工作如下:(1)本文提出一種基于核相關(guān)濾波器KCF(Kernelized Correlation Filters)的目標尺度估計以及處理遮擋的跟蹤方法SOD-KCF(Scale Estimation and Occlusion Detection-KCF)。目標尺度估計模型對當(dāng)前目標進行多尺度化,通過計算下一時刻各個尺度對應(yīng)的分類器響應(yīng)最大值構(gòu)成一個序列,選取最大值并將其對應(yīng)的尺度作為當(dāng)前目標跟蹤尺度。本文根據(jù)前向分類器響應(yīng)最大值的分布特征建立遮擋處理模型,其采用閾值方法進行遮擋檢測,在目標受到遮擋之后通過塊區(qū)域螺旋搜索方法進行目標搜索,在目標搜索過程中計算滑動框的響應(yīng)判定是否為目標。本文算法在OTB(Object Tracking Benchmark)測試序列集上測試并與4種跟蹤算法進行對比實驗,在跟蹤準確度與跟蹤成功率上分別比次優(yōu)方法提高了6.1%和1.5%。(2)為了進一步解決尺度變化和部分遮擋問題,本文在核相關(guān)濾波跟蹤算法基礎(chǔ)之上提出一種尺度空間濾波器以及分塊處理的方法MSKCF(Multi-Block and Scale Space-KCF)。此外,為了提高算法的魯棒性,本文建立一種外觀更新模型,該模型能夠近似目標遮擋和形變的變化狀態(tài)。此外,本文提出了一種自適應(yīng)更新學(xué)習(xí)率的模型,取代核相關(guān)濾波器算法原本固定的學(xué)習(xí)率,加強了跟蹤算法處理每個子塊的魯棒性。實驗結(jié)果表明,本文算法在OTB測試序列集上優(yōu)于其他對比的算法,特別地,在遮擋與尺度變化的測試序列中與核相關(guān)濾波跟蹤算法對比,分別有近8%和18%的性能提高。因此,本文算法對原有算法的改進具有準確性和有效性。
[Abstract]:In recent years, with the development of science and technology and the progress of society, the application of computer vision technology has been more and more popular in people's lives, most of which are based on video surveillance applications, schools, hospitals, Stations and other public places have a large number of surveillance cameras, providing security for people's social life. In addition, traffic monitoring has become more and more intelligent. Intelligent probes such as roads and railways have been able to solve many problems automatically. And can record the key events, most of the technology of intelligent monitoring application depends on computer vision theory, and one of the branches of computer vision target tracking has become a hot spot in recent years, but in the real scene, There are many difficulties in target tracking, such as occlusion, multi-scale change of target, illumination change and so on. The purpose of this paper is to study on the basis of previous work. This paper mainly solves the two problems of occlusion and scale change in the process of target tracking. The main work is as follows: (1) this paper proposes a target scale estimation based on Kernel correlation filter (KCF (Kernelized Correlation Filters) and a tracking method (SOD-KCF (Scale Estimation and Occlusion Detection-KCF) to deal with occlusion. The target scale estimation model carries on the multi-scale to the current target. By calculating the maximum value of the classifier response corresponding to each scale at the next time, a sequence is formed, and the maximum value is selected and the corresponding scale is taken as the current target tracking scale. In this paper, the occlusion processing model is established according to the distribution characteristics of the maximum response of the forward classifier. The occlusion detection is carried out by the threshold method, and the target search is carried out by the block region spiral search method after the target is occluded. In the process of target search, the response of the sliding frame is calculated to determine whether the target is the target. This algorithm is tested on the OTB (Object Tracking Benchmark) test sequence set and compared with four tracking algorithms. The tracking accuracy and the tracking success rate are improved by 6.1% and 1.5% respectively. (2) in order to solve the problem of scale variation and partial occlusion further, Based on the kernel correlation filter tracking algorithm, a scale-space filter and block processing method MSKCF (Multi-Block and Scale Space-KCF) is proposed in this paper. In addition, in order to improve the robustness of the algorithm, a new appearance updating model is established, which can approximate the changing state of the object occlusion and deformation. In addition, this paper proposes an adaptive update learning rate model, which replaces the original fixed learning rate of the kernel correlation filter algorithm, and enhances the robustness of the tracking algorithm to deal with each sub-block. The experimental results show that the proposed algorithm is superior to other contrast algorithms in OTB test sequence sets. In particular, the performance of the proposed algorithm is improved by nearly 8% and 18%, respectively, compared with the kernel correlation filter tracking algorithm in the test sequence of occlusion and scale variation. Therefore, this algorithm is accurate and effective to improve the original algorithm.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2018
【分類號】：TP391.41;TN713

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本文編號：2195163