發(fā)布時間：2018-03-25 19:52

  本文選題：目標跟蹤　切入點：相關濾波　出處：《安徽大學》2017年碩士論文

【摘要】：作為計算機視覺領域最具挑戰(zhàn)的關鍵技術(shù)之一,目標跟蹤在視頻監(jiān)控、導航、軍事、人機交互、虛擬現(xiàn)實、智能機器人、自動駕駛等多個領域都有著廣泛的應用。經(jīng)過三十多年的研究,目標跟蹤領域相繼涌現(xiàn)了大量經(jīng)典、優(yōu)秀的跟蹤算法,但受限于現(xiàn)實環(huán)境以及目標運動的復雜性,當前的跟蹤算法在準確性、魯棒性以及實時性上難以滿足實際的應用需求。準確、魯棒、高效的目標跟蹤算法仍然是極具挑戰(zhàn)的研究課題。相關濾波跟蹤(correlationtracking)自提出以來,其就以兼顧準確性和速度的優(yōu)勢,吸引了大量研究者的關注。相關濾波器通過傅里葉變換將濾波器操作轉(zhuǎn)換到頻域,極大提升了算法運行速度,實現(xiàn)了目標位置中心的快速檢測,并且其重新采樣在線更新濾波器,保證了算法的準確度和實時性。本文深入研究了基于相關濾波的目標跟蹤算法,針對特征融合、尺度估計以及濾波器更新策略進行改進,并在此基礎之上,結(jié)合相關濾波器跟蹤狀態(tài)判斷和級聯(lián)目標檢測,實現(xiàn)了穩(wěn)定的長時間目標跟蹤。本文主要的研究內(nèi)容和創(chuàng)新點總結(jié)如下:(1)首先介紹了目標跟蹤領域的研究背景與意義、發(fā)展現(xiàn)狀以及技術(shù)挑戰(zhàn),并歸納總結(jié)當前目標跟蹤領域主流的算法框架,然后概述相關濾波器的基本概念及其在目標跟蹤上的應用原理。(2)為了提高相關濾波跟蹤的精度和成功率,提出了基于相關濾波的尺度和學習率自適應跟蹤算法。首先算法融合了高效的特征提取方法作為濾波器輸入目標樣本的外觀表示;針對相關濾波器不能應對目標尺度變化的限制,結(jié)合光流跟蹤的思路,根據(jù)相鄰幀之間可靠關鍵點的位移變化估計目標尺度;并采用學習率自適應方法,改進相關濾波器的更新策略。通過高效的特征提取、尺度估計以及學習率自適應方法的綜合運用,大幅提升了跟蹤準確度,同時也相對節(jié)省了算法運算量,保證跟蹤器的實時性。在ObjectTrackingBenchmark上進行算法的對比實驗、成分分析實驗以及定性評估實驗,以驗證算法改進的有效性。(3)針對長時間跟蹤過程中面臨的難題和挑戰(zhàn),提出了基于相關濾波和級聯(lián)檢測的長時間目標跟蹤算法。首先采用基于相關濾波跟蹤的改進算法作為基礎跟蹤器,并結(jié)合跟蹤目標狀態(tài)判斷、級聯(lián)檢測丟失目標的策略,組成長時間跟蹤的算法框架。其中級聯(lián)檢測器分別包括基于顏色模型的局部檢測、最近鄰檢測以及微調(diào)三個模塊,高效的跟蹤目標狀態(tài)判斷方法則是能夠及時啟動級聯(lián)檢測器的關鍵。通過級聯(lián)檢測逐層篩選搜索樣本,找回丟失的跟蹤目標,提升了算法在長時間跟蹤中的穩(wěn)定性。
[Abstract]:As one of the most challenging key technologies in the field of computer vision, target tracking in video surveillance, navigation, military, human-computer interaction, virtual reality, intelligent robot, Autopilot and other fields have been widely used. After more than 30 years of research, a large number of classic and excellent tracking algorithms have emerged in the field of target tracking, but limited by the real environment and the complexity of target motion. The current tracking algorithms are difficult to meet the practical application requirements in accuracy, robustness and real-time. Accurate, robust and efficient target tracking algorithm is still a challenging research topic. With the advantage of both accuracy and speed, it has attracted the attention of a large number of researchers. The correlation filter transforms the filter operation into frequency domain by Fourier transform, which greatly improves the speed of the algorithm. The fast detection of target location center is realized, and its resampling online update filter ensures the accuracy and real-time of the algorithm. In this paper, the target tracking algorithm based on correlation filter is studied in depth, aiming at feature fusion. Scale estimation and filter update strategy are improved, and based on this, correlation filter tracking state judgment and cascade target detection are combined. The main research contents and innovations of this paper are summarized as follows: first, the background and significance of the research in the field of target tracking, the current situation of development and the technical challenges are introduced. Then the basic concept of correlation filter and its application in target tracking are summarized. In order to improve the accuracy and success rate of correlation filter tracking, the paper summarizes the main algorithm framework in the field of target tracking, and then summarizes the basic concept of correlation filter and its application in target tracking. The scale and learning rate adaptive tracking algorithm based on correlation filter is proposed. Firstly, the efficient feature extraction method is used as the appearance representation of the filter input target sample. In view of the fact that the correlation filter can not cope with the limitation of the change of target scale, combined with the idea of optical flow tracking, the target scale is estimated according to the displacement change of reliable key points between adjacent frames, and the learning rate adaptive method is adopted. The updating strategy of correlation filter is improved. By using efficient feature extraction, scale estimation and adaptive learning rate method, the tracking accuracy is greatly improved, and the computational complexity of the algorithm is also saved. In order to verify the effectiveness of the improved algorithm, the real-time performance of the tracker is verified by the contrast experiment of algorithm, component analysis experiment and qualitative evaluation experiment on ObjectTrackingBenchmark to solve the problems and challenges in the long time tracking process. A long time target tracking algorithm based on correlation filtering and concatenated detection is proposed. Firstly, the improved algorithm based on correlation filter is used as the basic tracker. The cascade detector consists of three modules: local detection based on color model, nearest neighbor detection and fine tuning. The efficient tracking target state judgment method is the key to start the cascade detector in time. Through cascading detection to filter the search samples layer by layer, the missing target can be retrieved, and the stability of the algorithm in the long time tracking is improved.
【學位授予單位】：安徽大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關期刊論文 前1條

1 胡昭華;邢衛(wèi)國;何軍;張秀再;;多通道核相關濾波的實時跟蹤方法[J];計算機應用;2015年12期

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本文編號：1664590