本研究專注於結合人臉識別技術與 Pan-Tilt-Zoom (PTZ) 攝影機控制，開發智慧場館的即時互動系統，並針對現有以西方數據為主的人臉辨識模型局限性，建立了一套在地化的臺灣人臉部數據集。透過深度學習演算法，實現即時辨識「誰是大明星」的互動遊戲，並結合自動化攝影機控制技術，動態追蹤並顯示選定臉孔於場館螢幕。
研究內容包括建構多樣化臺灣名人臉部數據集，並運用 YOLO 演算法進行人臉檢測與 Deep SORT 進行多目標追蹤，結合特徵匹配技術進一步開發 PTZ 攝影機的動態控制機制。實驗結果顯示，若採用多來源數據集進行混合訓練，可大幅增強模型的跨族群辨識泛化能力。特別是在台灣名人測試集中（TWFD），AUC 高達 0.994。PTZ 攝影機控制測試結果顯示，系統平均僅需約 2.87 秒即可自動將目標臉孔平移至畫面中央，反應速度約為人工操作所需時間（8.3 秒）的三分之一。自動控制過程中僅進行少量移動即可完成畫面構圖，展現出良好的即時性與效率。相較之下，傳統手動追蹤需多次調整方能完成同一任務，顯示本系統於實場環境中具備顯著的控制優勢與穩定性。
本系統展現了創新的互動娛樂應用，能顯著提升觀眾參與感，並於演唱會、展覽等大型活動中具備實際應用潛力。同時，本研究也為多族群人臉辨識與智慧場館技術的整合應用開創新方向，並進一步探討未來在資料擴充與商業化應用上的可能性。

This study presents the development of a real-time interactive system for smart venues by integrating facial recognition technology with Pan-Tilt-Zoom (PTZ) camera control. In response to the limitations of existing models that rely heavily on Western-centric datasets, a localized facial dataset featuring Taiwanese was constructed to improve recognition performance in regional contexts. Leveraging deep learning techniques, the system enables real-time “Celebrity look alike” interaction, dynamically identifying look-alike spectators and controlling PTZ cameras to display target faces on venue screens.
The system design includes the construction of a diverse facial dataset representing Taiwanese public figures of various genders and facial characteristics. It employs YOLO for real-time face detection, Deep SORT for multi-object tracking, and feature matching to drive adaptive PTZ camera movements. Experimental results show that training with a combined dataset significantly enhances cross-group generalization. Specifically, the model achieved an AUC of 0.994 on the TWFD test set. In PTZ camera tests, the system automatically centered the target face within an average of 2.87 seconds—approximately one-third the time required for manual control—using minimal movement to complete the framing task. This efficiency underscores the system’s high responsiveness and operational stability in real-world deployments.
Overall, the proposed system demonstrates innovative applications in interactive entertainment, significantly improving audience engagement and offering practical feasibility for large-scale events such as concerts and exhibitions. Furthermore, this work contributes to the integration of multi-ethnic facial recognition and smart venue technologies, laying a foundation for future research in dataset expansion, cross-cultural adaptability, and commercial deployment.

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