【摘要】：隨著社會(huì)的信息化水平日益提高,傳統(tǒng)產(chǎn)業(yè)開始利用信息技術(shù)來(lái)提高生產(chǎn)效率、減少人力消耗,而計(jì)算機(jī)視覺技術(shù)已經(jīng)越來(lái)越多的被應(yīng)用于民用領(lǐng)域和軍事領(lǐng)域,生物特征識(shí)別、智能監(jiān)控、無(wú)人駕駛、智能武器等新興的概念開始不斷升溫。其中,視頻目標(biāo)跟蹤技術(shù)是計(jì)算機(jī)視覺領(lǐng)域中的一個(gè)經(jīng)典研究課題,但是由于實(shí)際場(chǎng)景中往往存在光照變化、運(yùn)動(dòng)狀態(tài)突變、目標(biāo)遮擋、相似物體干擾等復(fù)雜情況,當(dāng)前已有的目標(biāo)跟蹤技術(shù)仍難以滿足實(shí)際應(yīng)用的需求。目標(biāo)跟蹤問(wèn)題可以看作是由感興趣目標(biāo)先前得知的位置來(lái)預(yù)測(cè)其在后續(xù)視頻序列中的空間位置,這是一個(gè)根據(jù)先驗(yàn)條件來(lái)對(duì)當(dāng)前狀態(tài)進(jìn)行估計(jì)、驗(yàn)證的過(guò)程,因此可以利用貝葉斯?fàn)顟B(tài)估計(jì)的思想來(lái)對(duì)問(wèn)題進(jìn)行求解。本文正是對(duì)于其中最為經(jīng)典的粒子濾波算法進(jìn)行研究,探討了視頻目標(biāo)跟蹤中的一些關(guān)鍵性問(wèn)題,主要的創(chuàng)新工作與研究成果包括以下幾方面:1.針對(duì)傳統(tǒng)粒子濾波目標(biāo)跟蹤方法中粒子的多樣性不足以及易受場(chǎng)景干擾的問(wèn)題,提出一種改進(jìn)的免疫粒子濾波目標(biāo)跟蹤方法,該方法基于人工免疫算法的思想,根據(jù)目標(biāo)跟蹤中的關(guān)鍵性問(wèn)題加入了抗體記憶庫(kù)、粒子集可信度判定等過(guò)程,以提高算法在較復(fù)雜場(chǎng)景中的魯棒性。2.建立合理的目標(biāo)模型是粒子集更新結(jié)果趨向于目標(biāo)狀態(tài)真實(shí)值的重要前提,本文針對(duì)傳統(tǒng)算法中的單一目標(biāo)模型適應(yīng)性較差的問(wèn)題,提出了加入自適應(yīng)學(xué)習(xí)機(jī)制的外觀模型與運(yùn)動(dòng)模型,同時(shí)利用了特征分片、背景權(quán)重等思想,并且給出了相應(yīng)的似然性計(jì)算方法。3.針對(duì)單目標(biāo)粒子濾波跟蹤方法直接應(yīng)用到多目標(biāo)跟蹤問(wèn)題時(shí)易出現(xiàn)的問(wèn)題,提出了一個(gè)快速的交互目標(biāo)判定與匹配算法,該方法適用于粒子濾波框架下的跟蹤方法,可以在一定程度上提高多目標(biāo)跟蹤的準(zhǔn)確性。本文嘗試通過(guò)對(duì)傳統(tǒng)的粒子濾波目標(biāo)跟蹤算法進(jìn)行改進(jìn),使其在較為復(fù)雜的實(shí)際場(chǎng)景中提高性能。分別在Visual Tracker Benchmark測(cè)試庫(kù)、PETS 2009Benchmark Data測(cè)試庫(kù)以及車載相機(jī)拍攝的動(dòng)態(tài)場(chǎng)景中選擇了多段典型的視頻進(jìn)行算法的對(duì)比實(shí)驗(yàn)與分析,通過(guò)L1-偏差、目標(biāo)區(qū)域覆蓋比、多目標(biāo)跟蹤精確度、算法運(yùn)行速度等統(tǒng)計(jì)指標(biāo)驗(yàn)證了所提算法較傳統(tǒng)方面具有明顯的提高,在實(shí)際場(chǎng)景中達(dá)到了較好的適應(yīng)性、魯棒性和實(shí)時(shí)性。
[Abstract]:With the increasing level of information technology in society, traditional industries begin to use information technology to improve production efficiency and reduce human consumption, and computer vision technology has been more and more used in civilian and military fields. New concepts such as biometric identification, intelligent surveillance, driverless and intelligent weapons are starting to heat up. Among them, video target tracking technology is a classical research topic in the field of computer vision. However, because of the complex situation, such as illumination change, moving state mutation, object occlusion, similar object interference and so on, in the actual scene, video target tracking technology often exists in the field of computer vision. The existing target tracking technology is still difficult to meet the needs of practical applications. The target tracking problem can be regarded as predicting the spatial position of the object of interest in the subsequent video sequence from the position previously known, which is a process of estimating and verifying the current state according to a priori condition. Therefore, Bayesian state estimation can be used to solve the problem. In this paper, the most classical particle filter algorithm is studied, and some key problems in video target tracking are discussed. The main innovative work and research results include the following aspects: 1. Aiming at the shortage of particle diversity and the vulnerability to scene interference in traditional particle filter target tracking methods, an improved immune particle filter target tracking method is proposed, which is based on the idea of artificial immune algorithm. According to the key problems in target tracking, the antibody memory library and particle set reliability evaluation are added to improve the robustness of the algorithm in more complex scenarios. 2. Establishing a reasonable target model is an important prerequisite for updating the result of particle set towards the real value of target state. This paper aims at the problem of poor adaptability of single objective model in traditional algorithm. The appearance model and motion model with adaptive learning mechanism are put forward, and the idea of feature segmentation and background weight are used, and the corresponding likelihood calculation method is given. 3. Aiming at the problem that single target particle filter tracking method is easy to appear when it is directly applied to multi-target tracking problem, a fast interactive target determination and matching algorithm is proposed, which is suitable for tracking method under particle filter framework. The accuracy of multi-target tracking can be improved to some extent. This paper attempts to improve the traditional particle filter target tracking algorithm to improve its performance in more complex practical scenarios. In the dynamic scene of Visual Tracker Benchmark test library, PETS 2009Benchmark Data test library and vehicle camera, we choose several typical video to carry on the contrast experiment and analysis, through L1-deviation, coverage ratio of target area, precision of multi-target tracking. The algorithm running speed and other statistical indicators verify that the proposed algorithm has obvious improvement compared with the traditional algorithm, and achieves better adaptability, robustness and real-time performance in the actual scene.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 李文輝;陳昱昊;王瑩;;自適應(yīng)的免疫粒子濾波車輛跟蹤算法[J];吉林大學(xué)學(xué)報(bào)(理學(xué)版);2016年05期

2 林濤;劉以安;;自適應(yīng)蟻群算法在多目標(biāo)跟蹤中的應(yīng)用[J];計(jì)算機(jī)仿真;2014年09期

3 肖沁雨;;智能視頻監(jiān)控關(guān)鍵技術(shù)分析[J];制造業(yè)自動(dòng)化;2012年12期

4 蔣戀華;甘朝暉;蔣e，

本文編號(hào)：2320631