大多數方法通過追蹤-檢測範式來解決多目標追蹤(MOT)問題，該範式通 過關聯檢測框的分數高於給定閾值的檢測窗口中的對象來追蹤對象。因此， 在處理複雜情況(如遮擋)時，置信度成為邊界框的唯一指標。然而，高置 信度不能保證邊界框與附近物體沒有重疊，尤其是在擁擠的場景中。在本論 文中，我們引入了一種更可靠的遮擋指標稱為 SU。此外，我們提出了一種 簡單的基於 Transformer 的目標重識別框架，與遮擋管理協同工作並處理遮 擋。首先，統計分析顯示空間不確定性與遮擋比例高度相關，能夠更好地表 示檢測框中的遮擋程度。所提出的兩階段 SSUTracker(具有空間不確定性的 稀疏追蹤器)在第一階段測量空間不確定性，然後在第二階段用於學習強健 的軌跡表示和關聯。結果，我們的方法在流行的 MOT16, MOT17 和 MOT20 基準測試中相比於最先進的方法取得了非常有競爭力的結果。

Most methods address the multi-object tracking (MOT) problem by tracking-by- detection paradigm, which tracks objects from the detected windows by associating detection boxes whose scores are higher than a given threshold. As such, the confi- dence score becomes the only indicator to bounding boxes when handling compli- cated cases, such as occlusions. However, high confidence score cannot guarantee that the bounding box has no overlapping with nearby obects, especially in crowded scenarios. In this thesis, we propose a reliable occlusion indicator called SU. Sta- tistical analysis indicates high correlation between spatial uncertainty and occlusion ratio, providing good indication of occlusion level in the detection results. In addi- tion, we propose a two-stage SSUTracker (Sparse tracker with Spatial Uncertainty), which measures spatial uncertainty in stage one and learns robust tracklet represen- tation and association with the SU for each object in stage two. Additionally, we propose a transformer-based ReID model to synergize with occlusion management and handle occlusions. As a result, our approach achieves very competitive results on popular MOT16, MOT17 and MOT20 benchmarks compared to state-of-the-art methods.

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