多相機多物件追蹤利用多視角的豐富畫面資訊更好地解決遮擋或擁擠場景中的追蹤問題。近年來，在物件追蹤領域中，以圖結構作為基底的研究方法漸漸盛行，然而這類的方法並沒有有效地利用時間與空間資訊上的一致性，再從多相機的角度去解決問題，他們更依賴以單相機追蹤模型產生的結果作為輸入，這樣會導致很多的軌跡碎片化或 ID 交換的錯誤發生。在這篇論文中，我們提出了一個創新的可重構時空圖模型，首先將所有相機視角下偵測到的物件建立一個空間圖並進行空間上的配對，再利用重構圖的模組轉換成時間圖，最後進行時間上的配對完成追蹤。兩階段的配對可以讓我們分別提取出空間與時間的特徵並有效地解決軌跡碎片化的問題。此外，我們的模型是設計成線上追蹤，更加適合應用在現實生活中的場景，實驗結果也顯示我們的模型可以提取出更具識別性的特徵，並在多個資料集上超越其他方法，達到最好的表現。

Multi-Camera Multi-Object Tracking (MC-MOT) utilizes information from multiple views to better handle problems with occlusion and crowded scenes. Recently, the use of graph-based approaches to solve tracking problems has become very popular. However, many current graph-based methods do not effectively utilize information regarding spatial and temporal consistency. Instead, they rely on single-camera trackers as input, which are prone to fragmentation and ID switch errors. In this thesis, we propose a novel reconfigurable graph model that first associates all detected objects across cameras spatially before reconfiguring it into a temporal graph for Temporal Association. This two-stage association approach enables us to extract robust spatial and temporal-aware features and address the problem with fragmented tracklets. Furthermore, our model is designed for online tracking, making it suitable for real-world applications. Experimental results show that the proposed graph model is able to extract more discriminating features for object tracking, and our model achieves state-of-the-art performance on several public datasets.

摘要
目錄
第一章-----------------------1
第二章-----------------------6
第三章-----------------------8
第四章----------------------21
第五章----------------------36
參考文獻--------------------37

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