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姓名	羅文霖(Wen-Lin Lo)  查詢紙本館藏	畢業系所	資訊工程學系在職專班
論文名稱	應用駕駛行為特徵於車輛再識別技術 (Using driving pattern for vehicle re-identification)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-1-30以後開放)
摘要(中)	影像監控用於我國偵辦案件中，是一個追朔還原事實真相之重大利器，同時也適合用於追蹤過去犯案歷史軌跡，目前我國多用車牌辨識攝影機進行快速查找對象行經軌跡，然受限攝影機視角需求及成本考量等因素，無法全面汰換佈設車牌辨識攝影機；而未佈設車牌辨識攝影機之路段中，偵辦人員仍以肉眼辨識案關車輛，並調閱多個路口監視器影像，藉由外觀達成車輛追蹤之目的，因此，本論文參考車輛再識別技術，其利用車輛外觀達到不同攝影機、角度及不同時間點之影像相似度比對，達成快速過濾目標車輛之目的，應有助於快速檢索可疑車輛，並作為第一線過濾篩選。 然因車輛再識別相關研究與實務上視訊監控系統環境有些差異，主要便是標的也是以轎車為主，針對公車或是貨車之再識別需求也不高，因此，本論文為將該技術貼合實際視訊監控系統，將測試資料集先行做物件偵測，抽取車輛物件以模擬其他視訊監控系統辨識情形，並排除了公車及貨車等標記資料，形成新的資料集，再以影像連續時間相關連之車輛物件進行疊合，並成為時空相關之車身動態資料，除了利用車輛再識別之技術除了抽取訓練資料中之外觀特徵外，本論文另設計動態特徵網路層，藉由連續時間影像及車輛動態位移資料，抽取車身動態特徵，整體模型透過車身動態及車輛外觀之特徵，提升轎車類型車輛再識別之準確度，而本論文研究實驗環境係以視訊監控系統作為前提假設，對於未來使用再識別技術之研究，若也是介接至視訊監控系統，應有其參考價值。
摘要(英)	Video surveillance is used in the investigation and handling of cases in our country. It is a great tool to trace back and restore the truth. It is also suitable for tracking the historical trajectory of past crimes. At present, license plate recognition cameras are often used in my country to quickly find the trajectory of objects, but the camera angle of view is limited. Due to factors such as cost considerations and other factors, it is impossible to fully replace and deploy license plate recognition cameras. In road sections without license plate recognition cameras, investigators still identify suspicious vehicles with the naked eye, and call multiple intersection monitor images to identify vehicles based on their appearance. Therefore, this paper refers to the vehicle re-identification technology, which uses the appearance of the vehicle to achieve the image similarity comparison of different cameras, angles and different time points, to achieve the purpose of quickly filtering the target vehicle, which should help to quickly retrieve suspicious vehicles as the first line of filtering. However, there are some differences between the research on vehicle re-identification and the actual environment of the video surveillance system. The main target is cars, and the demand for re-identification of buses or trucks is not high. Therefore, this paper aims to fit the technology In the actual video surveillance system, the test data set is used for object detection first, and vehicle objects are extracted to simulate the recognition situation of other video surveillance systems, and the marked data such as buses and trucks are excluded to form a new data set, which is then correlated with the continuous time of images The vehicle objects are superimposed and become the dynamic data of the vehicle body related to time and space. In addition to using the technology of vehicle re-identification to extract the appearance features in the training data, this paper also designs a dynamic feature network layer, through continuous time images and vehicles. The dynamic displacement data extracts the dynamic characteristics of the vehicle body. The overall model improves the accuracy of re-identification of sedan-type vehicles through the characteristics of vehicle body dynamics and vehicle appearance. The experimental environment of this paper is based on the premise of the video surveillance system. For future use of re-identification If the technical research is also connected to the video surveillance system, it should have its reference value.
關鍵字(中)	★ 車輛再識別 ★ 動態特徵 ★ 再識別技術	關鍵字(英)	★ vehicle re-identification ★ motion pattern ★ re-id
論文目次	摘　要 i Abstract iii 目錄 v 圖目錄 vii 表目錄 ix 第一章、緒論 1 1.1. 研究背景 1 1.2. 研究目標 1 1.3. 論文架構 2 第二章、文獻回顧 4 2.1. 車輛再識別技術相關研究 4 2.2. VeRi-776資料集介紹 5 2.3. 車輛再識別常用之損失函數 6 2.4. 網路模型 7 2.5. 模型評估驗證指標 7 第三章、再識別神經網路實驗架構設計 9 3.1. 實驗基準 9 3.2. 影像純化 10 3.3. 資料輸入方式設計 11 3.4. 實驗環境設計 12 3.5. 網路模型設計 19 第四章、網路模型實驗 26 4.1. 實驗環境 26 4.2. 實驗基準 28 4.3. 性能優化實驗 30 4.4. 動態特徵網路模型實驗 33 第五章、結論與未來展望 37 5.1. 結論 37 5.2. 未來展望 38 參考文獻 39
參考文獻	[1] 李冠霖, 警察調閱路口監視器面臨問題與因應對策之研究, 中央警察大學刑事警察研究所, 2018. [2] 蘇保安, 警政機關監視錄影系統策略運用之 成效評估, 中央警察大學刑事警察研究所, 102. [3] 陳聲華, 應用錄影監視系統提升犯罪偵查效用-以台北市為例, 中央警察大學刑事警察研究所, 105. [4] 羅元佑, 警察人員調閱監視影像思維之研究, 中央警察大學刑事警察研究所, 2022. [5] Ratnesh Kumar, Edwin Weill, Farzin Aghdasi and Parthasarathy Sriram, "Vehicle re-identification: an efficient baseline using triplet embedding," in 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019. [6] Yan Bai, Yihang Lou, Feng Gao, Shiqi Wang, Yuwei Wu and Ling-Yu Duan, "Group-sensitive triplet embedding for vehicle reidentification," IEEE Transactions on Multimedia, vol. 20, no. 9, pp. 2385--2399, 2018. [7] Zhedong Zheng, Tao Ruan, Yunchao Wei, Yi Yang and Tao Mei, "VehicleNet: Learning robust visual representation for vehicle re-identification," IEEE Transactions on Multimedia, vol. 23, pp. 2683--2693, 2020. [8] Zhun Zhong, Liang Zheng, Donglin Cao and Shaozi Li, "Re-ranking person re-identification with k-reciprocal encoding," in Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, 2017. [9] Fangyu Wu, Shiyang Yan, Jeremy S. Smith and Bailing Zhang, "Joint semi-supervised learning and re-ranking for vehicle re-identification," in 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2018. [10] Chiyomi Miyajima, Yoshihiro Nishiwaki, Koji Ozawa, Toshihiro Wakita, Katsunobu Itou, Kazuya Takeda and Fumitada Itakura, "Driver modeling based on driving behavior and its evaluation in driver identification," Proceedings of the IEEE, vol. 95, no. 2, pp. 427--437, 2007. [11] Clara Marina Martinez, Mira Heucke, Fei-Yue Wang, Bo Gao and Dongpu Cao, "Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, pp. 666--676, 2017. [12] Hongbo Wang, Jiaying Hou and Na Chen, "A survey of vehicle re-identification based on deep learning," IEEE Access, vol. 7, pp. 172443--172469, 2019. [13] Sultan Daud Khan and Habib Ullah, "A survey of advances in vision-based vehicle re-identification," Computer Vision and Image Understanding, vol. 182, pp. 50-63, 2019. [14] Xinchen Liu, Wu Liu, Huadong Ma and Huiyuan Fu, "Large-scale vehicle re-identification in urban surveillance videos," in 2016 IEEE international conference on multimedia and expo (ICME), Seattle, WA, USA, 2016. [15] Xinchen Liu, Wu Liu, Tao Mei and Huadong Ma, "A deep learning-based approach to progressive vehicle re-identification for urban surveillance," in European conference on computer vision, 2016. [16] Xinchen Liu, Wu Liu, Tao Mei and Huadong Ma, "Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance," IEEE Transactions on Multimedia, vol. 20, no. 3, pp. 645--658, 2017. [17] Yiheng Zheng, Dong Liu and Zheng-Jun Zha, "Improving triplet-wise training of convolutional neural network for vehicle re-identification," in 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China, 2017. [18] Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang, Hongsheng Li and Xiaogang Wang, "Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification," in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017. [19] Dechao Meng, Liang Li, Xuejing Liu, Yadong Li, Shijie Yang, Zheng-Hun Zha, Xingyu Gao, Shuhui Wang and Qingming Huang, "Parsing-based view-aware embedding network for vehicle re-identification," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, 2020. [20] Shuting He, Hao Luo, Pichao Wang, Fan Wang, Hao Li and Wei Jiang, "TransReID: Transformer-based Object Re-Identification," in Proceedings of the IEEE/CVF international conference on computer vision, Montreal, QC, Canada, 2021. [21] Florian Schroff, Dmitry Kalenichenko and James Philbin, "FaceNet: A Unified Embedding for Face Recognition and Clustering," in Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 2015. [22] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin, "Attention is all you need," Advances in neural information processing systems, vol. 30, 2017. [23] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uskoreit and Neil Houlsby, "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020. [24] Josh Beal, Eric Kim, Eric Tzeng, Dong Huk Park, Andrew Zhai and Dmitry Kislyuk, "Toward transformer-based object detection," arXiv, 2020. [25] Kaiming He, Georgia Gkioxari, Piotr Dolla ́r and Ross Girshick, "Mask R-CNN," in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017. [26] Ching-Han Chen and Chao-Tsu Liu, "A 3.5-Tier Container-Based Edge Computing Architecture," Computers and Electrical Engineering, vol. 93, p. 107227, July 2021. [27] Ching-Han Chen, Ming-Yi Lin and Jhong-Ci Liou, "Edge Computing Gateway of Industrial Internet of Things Using Multiple Collaborative Microcontrollers," IEEE Network, vol. 32, no. 1, pp. 12-21, January/February 2018. [28] Ching-Han Chen, Ming-Yi Lin and Xing-Chen Guo, "High Performance Fieldbus Application-Specific Integrated Circuit Design for Industrial Smart Sensor Networks," Journal of Supercomputing, vol. 74, no. 9, pp. 4451-4469, September 2018. [29] Ching-Han Chen, Ming-Yi Lin and Xing-Chen Guo, "High-level Modeling and Synthesis of Smart Sensor Networks for Industrial Internet of Things," Computers & Electrical Engineering, vol. 61, pp. 48-66, July 2017. [30] Ching-Han Chen, Ming-Yi Lin and Yu-Chien Shih, "High-Precision Time Synchronization Chip Design for Industrial Sensor and Actuator Network," Microprocessors and Microsystems, vol. 91, June 2022. [31] Kaiwei Zeng, Munan Ning, Yaohua Wang and Yang Guo, "Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020. [32] Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo and Laura Leal-Taix ́e, "The Group Loss for Deep Metric Learning," arXiv, 2019. [33] Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805, 2018. [34] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens and Zbigniew Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 2016.
指導教授	陳?瀚(Ching-Han Chen)	審核日期	2023-1-17
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