採用以取樣為基礎的資料關聯技術於混合影像序列之多物件追蹤

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張庭豪(Ting-Hao Zhang) 查詢紙本館藏

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通訊工程學系

論文名稱

採用以取樣為基礎的資料關聯技術於混合影像序列之多物件追蹤
(Multiple Objects Tracking Using Sample-based Data Association for Mixed Images)

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摘要(中)

在進行物件追蹤時，當物件進到含有強烈鏡面反射(specular reflection)的影像區域時，容易因為物體外觀的強烈變化而降低追蹤的準確率，此外當進行多物件追蹤時，需計算每個量測資訊與物件的資料關聯(data association)，錯誤的量測資訊將降低追蹤的準確率，因此，本論文提出針對混合影像(mixed images)的以取樣為基礎的多物件追蹤演算法。首先，本論文提出簡化的RANSAC 方案用以估測相機動量，藉此提升補償運動模型(compensated motion model)與動量補償之多層分離(motion compensated layer separation)的效能。本論文採用以取樣為基礎的聯合機率資料關聯濾波器(sample-based joint
probabilistic data association filter)，結合共推論追蹤(co-inference tracking)後的物件狀態，計算物件與量測資訊的關聯度，提升資料關聯正確性。此外本論文提出最大聯合似然機率，利用最大似然法(maximum likelihood)以優化利用外觀資訊與軌跡資訊計算出的聯合
似然機率(joint likelihood)，並提出利用量測信心指數，提供物件的遮蔽資訊，提升共推論追蹤(co-inference tracking)於更正階段的準確率，最後本論文利用外觀相似度判斷，進行物件的外觀模型更新。實驗結果顯示，本論文提出的多物件追蹤演算法可有效克服不同強度的反射以及遮蔽的影響，有效提升追蹤系統的強健性及準確性。

摘要(英)

For object tracking, object moves into the region with strong specular reflections will decrease tracking accuracy because of the significant change of the target appearance. In addition, data association between measurements and objects is needed for multiple objects
tracking because the wrong measurement will decrease tracking accuracy. Thus, this thesis proposes a sample-based multiple objects tracking for mixed images. At first, this thesis proposes a simplified RANSAC method to estimate camera motion. It can promote the efficiency of compensated motion model and motion compensated layer separation. This thesis adopts the sample-based joint probabilistic data association filter that refers to the co-inference tracking based object state to improve accuracy of data association. In addition, this thesis
proposes to maximize the joint likelihood that considers appearance and trajectory information at the correction stage. This thesis also proposes occlusion confidence indicator to provide the occlusion information to improve the accuracy in co-inference tracking based correction stage. Finally, this thesis updates target appearance model according to the similarity of appearance model. Experimental results show that the proposed scheme can effectively improve robustness and accuracy under the variation of specular reflection and the occlusion condition.

關鍵字(中)

★ 多物件追蹤
★ 混合影像
★ 資料關聯
★ 共推論融合
★ 最大似然機率
★ 遮蔽
★ 聯合似然機率

關鍵字(英)

★ multiple objects tracking
★ mixed images
★ data association,
★ co-inference tracking
★ maximum joint likelihood,
★ occlusion

論文目次

摘要…………………………………………………………………………………………….I
Abstract..………………………………………………………………………………………II
誌謝…………………………………………………………………………………………...III
目錄…………………………………………………………………………………………..IV
圖目錄..………………………………………………………………………………………VI
表目錄…..……………………………………………………………………………………IX
第一章緒論 ............................................................................................................................ 1
1.1. 前言 ........................................................................................................................ 1
1.2. 研究動機 ................................................................................................................ 1
1.3. 研究方法 ................................................................................................................ 3
1.4. 論文架構 ................................................................................................................ 4
第二章物件追蹤之資料關聯技術現況 .................................................................................. 5
2.1 單物件追蹤(Single Object Tracking) ......................................................................... 5
2.1.1 貝氏濾波器(Basiyen Filter) ............................................................................. 5
2.1.2 粒子濾波器(Particle Filter, PF) ....................................................................... 7
2.1.3 機率資料關聯濾波器(Probability Data Association Filter, PDAF) ................ 8
2.2 多物件追蹤之資料關聯(Data Association)技術現況 ............................................. 11
2.2.1 物件與量測之資料關聯(Data Association) .................................................. 12
2.2.3 多物件與多量測之資料關聯 ......................................................................... 16
2.3 總結 ......................................................................................................................... 21
第三章混合影像序列下之追蹤技術現況 ............................................................................ 22
3.1 混合影像序列下之特徵點追蹤(Feature Point Tracking in Mixed Image) ............. 22
3.1.1 顏色獨立性分離(Layer Separation using Color Independence) ................... 23
3.1.2 本質影像分離(Layer Separation using Intrinsic Image) ............................... 23
3.2 混合影像序列之物件追蹤(Object Tracking in Mixed Sequences) ......................... 25
3.2.1 盲訊號分離之混合影像視覺追蹤(Visual Tracking Using Blind Source
Separation for Mixed Images) .................................................................................. 26
3.2.2 基於資訊融合的混合影像之強健性追蹤(Robust Tracking Using Visual Cue
Integration for Mobile Mixed Images) ..................................................................... 29
3.3 總結 ........................................................................................................................... 31
第四章本論文所提之混合影像序列的多物件追蹤方案 .................................................. 32
4.1 系統架構 ................................................................................................................... 32
4.2 混合影像分離與動態層遮罩之建立(Layer Separation of Mixed Images and
Constructions of Motion Masks) ...................................................................................... 33
4.2.1 相機動量估測(Estimation of Camera Motion) ............................................... 34
4.2.2 動態層遮罩之建立(Construction of Motion Mask) ...................................... 35
V
4.3 聯合似然機率之最大化(Maximum Joint Likelihood) ............................................. 36
4.4 以信心指數為基礎之量測可靠度計算(Calculation of Measurement Robustness
Based on The Confidence Indicator) ................................................................................ 40
4.5 物件外觀模型更新(Target Appearance Model Update) .......................................... 43
4.7 總結 ............................................................................................................................ 45
第五章實驗結果與討論 ........................................................................................................ 46
5.1 實驗參數與測試影片規格 ....................................................................................... 46
5.2 追蹤系統實驗結果 ................................................................................................... 47
5.2.1 ACF 行人偵測器實驗結果(Experimental Results of ACF Detector) .......... 48
5.2.2 方均根誤差之追蹤準確率(Tracking Accuracy with Root Mean Square Error)
.................................................................................................................................. 49
5.2.3 多物件追蹤準確之追蹤準確率(Tracking Accuracy with Multiple Object
Tracking Accuracy) .................................................................................................. 52
5.2.4 多物件追蹤精準(Multiple Object Tracking Precision, MOTP) ..................... 53
5.2.5 聯合似然機率之效能(Accuracy of Joint Likelihood)................................... 55
5.2.6 動態層空間資訊之效能(Accuracy of Spatial Information of Motion Mask)
.................................................................................................................................. 56
5.2.7 物件外觀模型更新之效能(Target Accuracy of Appearance Model Update )
.................................................................................................................................. 58
5.2.8 遮蔽信心指數為基礎的資料關聯(Occlusion Confidence Indicator Based
data association) ....................................................................................................... 60
5.2.9 時間複雜度(Time Complexity) ..................................................................... 61
5.3 總結 ........................................................................................................................... 63
第六章結論與未來展望 ...................................................................................................... 64
參考文獻 .................................................................................................................................. 65
Publications .............................................................................................................................. 70

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指導教授

唐之瑋(Chih-Wei Tang)

審核日期

2016-7-26

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