篇名 |
A Deep Learning-Based Person Search System for Real-World Camera Images
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並列篇名 | A Deep Learning-Based Person Search System for Real-World Camera Images |
作者 | Chih-Ta Yen、陳冠宇 |
英文摘要 | A person search system was developed to identify the query person from images captured by cameras at four scenes in the study. This study analyzed three network architectures called Model Basic, Model One, and Model Two. To verify the validity of the model design, the models in the public data set and in the recorded system data set were compared to determine whether the results of the proposed model exhibited consistent performance between the camera images from the public data set and the recorded, unprocessed system data set. The detected pedestrian images then underwent distance matching relative to query person images by using the online instance matching (OIM) loss function. Based on Model Basic, Model One and Model Two were designed to further improve accuracy by incorporating different convolutional neural networks. In CUHK-SYSU data set, the testing results of Model Basic, Model One and Model Two achieved the accuracies of 72.38%, 75.96% and 75.32%, respectively. The testing results of Model Basic, Model One, and Model Two with the system data set achieved accuracies of 63.745%, 68.80%, and 69.33%, respectively.
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起訖頁 | 839-851 |
關鍵詞 | Deep learning、Object detection、OIM loss function、Person search、ResNet50 |
刊名 | 網際網路技術學刊 |
期數 | 202207 (23:4期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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