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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅楸善 | |
| dc.contributor.author | Cheng-Shih Wong | en |
| dc.contributor.author | 翁丞世 | zh_TW |
| dc.date.accessioned | 2021-06-16T09:17:54Z | - |
| dc.date.available | 2022-07-17 | |
| dc.date.copyright | 2017-07-17 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-07-10 | |
| dc.identifier.citation | [1]. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,” IEEE Transactions on Signal Processing, volume 50, No. 2, pp. 174 – 188, 2002.
[2]. B. Babenko, M. H. Yang and S. Belongie, “Visual Tracking with Online Multiple Instance Learning,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, Florida, pp. 983 – 990, 2009. [3]. B. Benfold and I. Reid, “Stable Multi-Target Tracking in Real-Time Surveillance Video,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, Colorado, pp. 3457 – 3464, 2011. [4]. M. D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, and L. Van Gool, “Online Multiperson Tracking-By-Detection from a Single, Uncalibrated Camera,” IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 33, No. 9, pp. 1820 – 1833, 2011. [5]. D. Comaniciu, V. Ramesh, and P. Meer, “Real-time Tracking of Non-Rigid Objects Using Mean Shift,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, volume 2, pp. 142 – 149, 2000. [6]. D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-Based Object Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 25, No. 5, pp. 564 – 577, 2003. [7]. N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, California, volume 1, pp. 886 – 893, 2005. [8] F. F. Li, “CS231n: Convolutional Neural Networks for Visual Recognition,” http://cs231n.github.io/convolutional-networks/, 2017. [9]. H. Grabner, M. Grabner, and H. Bischof, “Real-Time Tracking via On-line Boosting,” Proceedings of British Machine Vision Conference, Edinburgh, UK, volume 1, pp. 47 – 56, 2006. [10]. M. Isard and A. Black, “CONDENSATION – Conditional Density Propagation for Visual Tracking,” International Journal of Computer Vision, volume 29, No. 5, pp. 5 – 28, 1998. [11]. Y. Q. Jia, S. Evan, D. Jeff, K. Sergey, L. Jonathan, G. Ross, G. Sergio, and D. Trevor, “Caffe: Convolutional Architecture for Fast Feature Embedding,” arXiv: 1408.5093, https://arxiv.org/pdf/1408.5093.pdf, 2014. [12]. Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-Learning-Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 6, No. 1, pp. 1409 – 1422, 2010. [13]. A. Krizhevsky, I. Sutskever, and G. E Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, pp. 1097 – 1105, 2002. [14]. N. S. Peng, J. Yang, and Z. Liu, “Mean Shift Blob Tracking with Kernel Histogram Filtering and Hypothesis Testing,” Pattern Recognition Letters, volume 26, No. 5, pp. 605 – 614, 2004. [15]. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv: 1409.1556, https://arxiv.org/pdf/1409.1556.pdf , 2014. [16]. Standford Vision Lab, “ImageNet Large Scale Vision Recognition Challenge,” http://www.image-net.org/challenges/LSVRC/, 2017. [17]. C. Stauffer and W. E. L. Grimson, “Adaptive Background Mixture Models for Real-Time Tracking,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, volume 2, pp. 246 – 252, 1999. [18]. T. H. Vu, A. Osokin, and I. Laptev, “Context-Aware CNNs for Person Head Detection,” Proceedings of International Conference on Computer Vision, Santiago, Chile, pp. 2893 – 2901, 2015. [19]. S. Tang, M. Andriluka, A. Milan, K. Schindle, S. Roth, and B. Schiele, “Learning People Detectors for Tracking in Crowded Scenes,” Proceedings of International Conference on Computer Vision, Sydney, Australia, pp. 1049 - 1056, 2013. [20]. Wikipedia, “Artificial Neural Network,” https://en.wikipedia.org/wiki/Artificial_neural_network, 2017. [21]. Wikipedia, “Backpropagation,” https://en.wikipedia.org/wiki/Backpropagation, 2017. [22]. Wikipedia, “Convolutional Neural Network,” https://en.wikipedia.org/wiki/Convolutional_neural_network, 2017. [23]. Wikipedia, “Massive Open Online Course,” https://en.wikipedia.org/wiki/Massive_open_online_course, 2017. [24]. Wikipedia, “Particle Filter,” https://en.wikipedia.org/wiki/Particle_filter, 2017. [25]. Y. Wu, J. Lim, and M. H. Yang, “Online Object Tracking: A Benchmark,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Oregon, Portland, pp. 2411 – 2418, 2013. [26]. B. Wu and R. Nevatia, “Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet Based Part Detectors,” International Journal of Computer Vision, volume 75, No. 2, pp. 247 – 266. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59209 | - |
| dc.description.abstract | 本論文開發一套即時的生醫教師追蹤與視訊錄影系統，能夠在室內環境中對特定目標教師以一廣角相機與一左右轉動上下傾斜與放大縮小 (PTZ, Pan-Tilt-Zoom) 相機來進行追蹤。
行人追蹤已經有許多的生活應用，例如:老年人看顧、線上即時會議、住宅安全監控。在此追蹤系統中，我們採用由偵測來追蹤的方法以粒子濾波的框架來實現。 首先我們必須指定欲追蹤目標，接著以粒子濾波來模擬目標位置的分布。我們訓練一卷積類神經網路來估計目標影像是否為人頭之機率，還有以背景相減法來做前景偵測，且以色彩直方統計計算候選與目標之相似度。最終以估計得最高機率位置為當前追蹤之目標，再迭代估計下一幀之位置。 | zh_TW |
| dc.description.abstract | In this thesis, we develop a nearly real-time biomedical teacher tracking and video recording system to track a biomedical teacher in the indoor scene by one wide-angle camera and one PTZ (Pan-Tilt-Zoom) camera.
Human tracking has many applications such as eldercare, security surveillance, and online meeting. In this human tracking system, we employ tracking-by-detection in particle filter framework to track the target. We have to specify which person to track first, and model the location of the target as a state distribution by particle filter. Moreover, we train a convolutional neural network as a head classifier to estimate the probability of human head, the motion detector with background subtraction, and color histogram is used to obtain the similarity between candidate and the target. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T09:17:54Z (GMT). No. of bitstreams: 1 ntu-106-R04945028-1.pdf: 2099894 bytes, checksum: 610616b72dbb1b25951c1eac73166ffb (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii Abstract iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Overview 1 1.2 Tracking-by-Detection 3 1.3 Convolutional Neural Network 4 1.4 Background Subtraction 4 1.5 Thesis Organization 5 Chapter 2 Related Works 6 2.1 Overview 6 2.2 Kernel-Based Tracking 7 2.3 Tracking-by-Detection 7 2.4 Domain-Specific Tracking 8 Chapter 3 Background 9 3.1 Overview 9 3.2 Particle Filter 9 3.3 Artificial Neural Network 13 3.4 Convolutional Neural Network 15 3.5 Background Subtraction 19 Chapter 4 Methodology 22 4.1 Overview 22 4.2 Particle Filter Framework 22 4.2.1 Particle Filter Sampling 24 4.2.2 Particle Filter Motion Estimation 25 4.2.3 Particle Filter Measurement 25 4.3 Background Subtraction 26 4.4 CNN as a Head Classifier 27 Chapter 5 Experimental Results 31 5.1 Overview 31 5.2 Evaluation 32 5.3 Results 33 Chapter 6 Conclusion 39 Chapter 7 References 40 | |
| dc.language.iso | en | |
| dc.subject | 背景相減法 | zh_TW |
| dc.subject | 視覺追蹤 | zh_TW |
| dc.subject | 教師追蹤 | zh_TW |
| dc.subject | 卷積類神經網路 | zh_TW |
| dc.subject | 色彩直方統計 | zh_TW |
| dc.subject | 偵測追蹤法 | zh_TW |
| dc.subject | 粒子濾波 | zh_TW |
| dc.subject | visual tracking | en |
| dc.subject | tracking-by-detection | en |
| dc.subject | color histogram | en |
| dc.subject | convolutional neural network | en |
| dc.subject | human tracking | en |
| dc.subject | background subtraction | en |
| dc.subject | particle filter | en |
| dc.title | 生醫教師追蹤與視訊錄影 | zh_TW |
| dc.title | Biomedical Teacher Tracking and Video Recording | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 趙坤茂,吳中皓,鄭宇哲 | |
| dc.subject.keyword | 視覺追蹤,教師追蹤,卷積類神經網路,色彩直方統計,偵測追蹤法,粒子濾波,背景相減法, | zh_TW |
| dc.subject.keyword | visual tracking,human tracking,convolutional neural network,color histogram,tracking-by-detection,particle filter,background subtraction, | en |
| dc.relation.page | 43 | |
| dc.identifier.doi | 10.6342/NTU201701450 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-07-11 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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