行人重識別技術旨在實現在不同攝影機和多個幀中跨攝影鏡頭追蹤和匹配人物的能力。該技術的應用非常廣泛，包括法務機關的嫌疑人追蹤、體育賽事的分析以及博物館人流分析等。然而，現有的重識別方法存在特徵提取和比對的限制，需要更多的計算資源且準確性有限。因此，研究人員致力於提出更準確、高效的方法，例如基於神經網路和地域分析的多攝影機人物追蹤分析方法，以改善人物重識別的準確性和實用性。
在人物辨識與追蹤方面，本研究使用了Yolov7來判斷人物位置，並使用StrongSORT技術來進行人物匹配追蹤。而為了提高重識別的速度與準確性，本研究設計了消失出現區域和視角消失出現時間兩種地域因素來限縮匹配範圍，並設計一個檢查機制，減少匹配錯誤的可能性。預期透過地域分析的幫助下，能給予現有的追蹤與重識別技術提供更精準的結果。
在實驗中，測試了地域分析下的OSNet和ResNet101重識別模型，相較直接對於全部的ID進行重識別的方法提高了約25%的準確度。特別是在地域分析的IDFR指標提高了29%的準確度，同時減少了運算壓力和時間，顯示地域分析演算法在實際應用中具有提升人物追蹤的效果，可帶來更準確、高效的人物追蹤和重識別解決方案。

Person re-identification technology aims to enable the ability to track and match individuals across different cameras and multiple frames. This technology has wide-ranging applications, including suspect tracking for law enforcement agencies, analysis of sports events, and crowd analysis in museums, among others. However, existing re-ID methods have limitations in feature extraction and matching, requiring more computational resources and having limited accuracy. Therefore, researchers are devoted to proposing more accurate and efficient approaches, such as neural network-based methods and geospatial analysis, to improve the accuracy and practicality of person re-identification.
In this research, YOLOv7 was employed for person detection to determine the positions of individuals, and the StrongSORT technique was utilized for person matching and tracking. In order to enhance the speed and accuracy of reidentification, two geographical factors were introduced: the concept of vanishing and reappearing regions and the perspective's vanishing and reappearing time. These factors were utilized to narrow down the matching scope. Additionally, a verification mechanism was designed to reduce the possibility of matching errors. It is expected that with the aid of geographical analysis, more precise results can be achieved for the existing tracking and reidentification techniques.
In the experiment, the OSNet (Omni-Scale Network) and ResNet101 recognition models were tested under geographical analysis. Compared to the direct recognition method on all IDs, they achieved an improvement of approximately 25% in accuracy. Particularly, the IDFR indicator in geographical analysis showed a 29% increase in accuracy, while reducing computational load and time. These results demonstrate that geographical analysis algorithms have the potential to enhance person recognition in practical applications, providing more accurate and efficient solutions for person tracking and recognition.

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