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研究生:陳慶豪
研究生(外文):Ching-Hau Chen
論文名稱:Visual Tracking through Occlusion Using Joint Appearance Models with Robust Statistics
論文名稱(外文):Visual Tracking through Occlusion Using Joint Appearance Models with Robust Statistics
指導教授:鮑興國鮑興國引用關係
指導教授(外文):Hsing-Kuo Pao
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:59
中文關鍵詞:Visual TrackingOcclusion HandlingParticle FilterRobust StatisticsJoint Appearance Models
外文關鍵詞:Visual TrackingOcclusion HandlingParticle FilterRobust StatisticsJoint Appearance Models
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Abstract
We suggest a method handling occlusion in visual tracking. The occlu-
sion handling method considers the joint appearance of overlapping objects
to estimate the position of the target. That is, we can track the object effec-
tively even it is occluded by other objects. For two objects, the appearance
of the overlapping area of two objects belongs to one of them. We use the
corresponding joint appearance to compute the likelihood in these two cases.
We select the maximum of two likelihoods as the winner to decide which is
occluding the other object. Besides, joint appearance model provides the
information about the depth ordering to help meaningfully setting the pa-
rameters for occluded or occluding cases. We also talk about the case, when
the object is occluded by background regions. We use robust statistics to
maintain the performance. We combine joint appearance model and robust
statistics in a unified framework to solve the occlusion problem.
Abstract
We suggest a method handling occlusion in visual tracking. The occlu-
sion handling method considers the joint appearance of overlapping objects
to estimate the position of the target. That is, we can track the object effec-
tively even it is occluded by other objects. For two objects, the appearance
of the overlapping area of two objects belongs to one of them. We use the
corresponding joint appearance to compute the likelihood in these two cases.
We select the maximum of two likelihoods as the winner to decide which is
occluding the other object. Besides, joint appearance model provides the
information about the depth ordering to help meaningfully setting the pa-
rameters for occluded or occluding cases. We also talk about the case, when
the object is occluded by background regions. We use robust statistics to
maintain the performance. We combine joint appearance model and robust
statistics in a unified framework to solve the occlusion problem.
Contents
1 Introduction 1
1.1 Problem proposed . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Visual Tracking 5
2.1 Visual tracking . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Particle filter . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Observation model . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 Difference between tracking and recognition . . . . . . . . . . 15
3 Occlusion Handling 18
3.1 Robust statistics . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Joint appearance model . . . . . . . . . . . . . . . . . . . . . 22
3.3 Joint appearance model with robust statistics . . . . . . . . . 26
4 Experimental Results 30
4.1 Ex1: two-persons tracking . . . . . . . . . . . . . . . . . . . . 31
4.2 Ex2: two-persons with background occlusion tracking . . . . . 32
4.3 Ex3: four-persons tracking in outdoor environment . . . . . . 40
5 Conclusion and future work 44
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
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