在本篇論文中，我們提出利用物體與其標記間的時間與空間相關性以呈現監視影片中前景物體的資訊。此外我們將計算標記的位置問題化為非線性最佳化的問題，並進一步使用馬可夫隨機場來解決此最佳化問題。據我們所知，此篇論文為首篇以前景物體的軌跡為單位，藉由前景物體與其標記關係來探討並計算標記位置的論文。如實驗所示，本論文所提出的方法可根據前景物體軌跡，並且在避免標記跟前景物體之間的重疊的條件下，計算標記在影片中的位置。我們的結果無論是在圖像上或是數據上，都較現有相關領域類似的研究更為優異。

In this paper, we propose a novel annotation placement approach for revealing information of foreground objects in surveillance videos. To arrange positions of annotations, spatiotemporal coherence between annotations and foreground objects is applied. The annotation placement problem is formulated as an optimization problem with respect to spatiotemporal coherence of annotations and foreground objects. The optimization problem is effectively solved by using Markov random fields (MRFs). To the best of our knowledge, this paper is the first work to discuss and solve the annotation placement problem for surveillance videos by considering the relationships between annotations and foreground objects with trajectories. As shown in the experiments, the proposed approach can arrange annotations based on the moving trajectories of foreground objects and prevent the occlusions between different annotations and foreground objects. It also achieves better quantitative and qualitative results compared to state-of-the-art approaches.

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