視訊濃縮為一種快速瀏覽影片的技術，非常適合用來處理冗長的原始監控影片，視訊濃縮技術藉由將所有移動的物件擷取出來，並進行時間或空間上的位移來去除冗餘的資訊，同時保留物件在原始影片中的活動與行為，然而過往的視訊濃縮技術中存在計算複雜度過高且費時的問題，並且在視訊濃縮結果中存在閃爍的現象。為了克服這些問題，我們提出了一種基於軌跡的高效率視訊濃縮系統，本系統不需要任何物件追蹤的演算法與能量最佳化的物件排列算法。在本論文中提出幾項演算法，首先提出了基於時空軌跡之快速物件分群法，能夠免除一般視訊濃縮系統中的不自然現象，接著利用基於時空域的最小碰撞軌跡圖進行物件串的排列，決定物件串在濃縮影片中的最佳出現時間點。此外，我們結合了視訊濃縮系統與基於卷積神經網路的物件偵測技術，讓使用者可以快速的找到特定物件。最後，我們透過大量的實驗證明本視訊濃縮的穩健性，且能夠高效率的產生免除閃爍現象的濃縮影片。

Video synopsis is a feasible solution to expedite browsing in raw surveillance data and to perform various video analytics tasks. The technique provides a condensed video with reduced spatial or temporal redundancies, without losing the actual content of the source video. However, conventional methods are computationally intensive and time-consuming and also with blinking effect in the resultant video. To overcome these problems, we propose a trajectory-based video synopsis system which can achieve high-performance without object tracking and energy optimization for tube rearrangement. In comparison to existing methods, Spatial-temporal trajectory-based object tube extraction algorithm is performed consistently in keeping tubes continuously to avoid blinking effect. Tube rearrangement based on Minimum Collision Trajectory in spatial-temporal domain is proposed to decide the best temporal position of tubes in synopsis video. Moreover, we integrate the object detection system based on convolutional neural network (CNN) with object tubes, which enables a user quickly locating a specific object. Finally, the proposed system can efficiently generate a condensed video without blinking effect, and its robustness validated with extensive experiments.

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