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Feature-based Particle Filter for Multiple Objects Tracking : 다중객체추적을 위한 특징 기반의 파티클 필터
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 유석인 | - |
dc.contributor.author | 서보경 | - |
dc.date.accessioned | 2017-07-14T02:47:38Z | - |
dc.date.available | 2017-07-14T02:47:38Z | - |
dc.date.issued | 2012-08 | - |
dc.identifier.other | 000000004831 | - |
dc.identifier.uri | https://hdl.handle.net/10371/122903 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2012. 8. 유석인. | - |
dc.description.abstract | This paper proposes an advanced particle filter for multi-target tracking with speed-up robust features. In this study, a mixture of the Gaussian Background Model and the SURF algorithm are used for target representation and localization. This approach transforms an image into a large collection of local feature vectors, each of which is invariant to the images translation, scaling, and rotation. Additionally, it is also partially invariant to illumination changes and affine or 3D projection. Lastly, NN algorithm is used for segmenting multiple objects into a single-object state space.
Several experimental results show that the proposed algorithm has good performance for object tracking in the presence of object translation, rotation and partial occlusion. Overall, this approach makes the system robust to occlusions and allows false positive detections in the background to be identified and removed. | - |
dc.description.tableofcontents | Chapter 1 Introduction
Chapter 2 Related Work 2.1 Object Detection 2.1.1 Geometry-based Object Detection 2.1.2 Appearance-based Object Detection 2.1.3 Feature-based Object Detection 2.2 Data Association 2.3 Multi-targets Tracking 2.3.1 Bayesian Filtering Chapter 3 Object Detection and Matching 3.1 Fast Interest Point Detection 3.2 Descriptor of Interest Point 3.3 Object Matching Chapter 4 Particle Filter for Object Tracking Chapter 5 Experimental Result 5.1 Environment 5.2 Result 5.2.1 Single Object Detection 5.2.2 Key Points Extraction 5.2.3 Key Points Matching 5.2.4 Multi-Objects Tracking 5.2.5 Comparing performance Chapter 6 Conclusion Bibliography Abstract | - |
dc.format | application/pdf | - |
dc.format.extent | 1263608 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Feature point | - |
dc.subject | SURF | - |
dc.subject | Particle Filter | - |
dc.subject | Tracking | - |
dc.title | Feature-based Particle Filter for Multiple Objects Tracking | - |
dc.title.alternative | 다중객체추적을 위한 특징 기반의 파티클 필터 | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | iv, 30 | - |
dc.contributor.affiliation | 공과대학 전기·컴퓨터공학부 | - |
dc.date.awarded | 2012-08 | - |
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