隨著科技日新月異，產品推陳出新，穿戴式攝影機越來越普及，例如：GoPro，Google Glass，HTC Re等，不僅體積小，影片的解析度也很清晰。隨著各式的攝影機，數以萬計的影片配放在網路上供人瀏覽。當攝影機沒有被固定在一個穩定的地方，而是隨著人的移動做拍攝時，一般的動作辨識可能就沒有辦法準確的分辨當下的行為。我們的目的是希望能在這種第一人稱的視角之下，也能做準確的分類。首先我們針對晃動的影片做穩定，以減少全域動量。接著在影片中找出含有動量的軌跡，沿著軌跡取HOG，HOF，MBH等特徵。接著針對這些特徵找出他們在時間軸以及空間軸的代表方式(Fisher vector 與 VideoDarwin)，最後用SVM當分類器，判別動作的種類。

With the popularity of portable devices, the number of egocentric videos increase. Unstructured movement of the camera due to natural head motion of the first-person causes sharp changes in the visual field of the egocentric camera causing many traditional action recognition methods to perform poorly in these scenario. This paper proposed a new approach for action recognition in First-Person videos by decomposing the dense trajectories and finding a better representation for spatial and temporal informations by fisher vector and ranking machine. Simulation results show the robustness of the proposed method which outperform the other handcraft feature approaches.

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