Event recognition plays an essential role to conduct a high-performing content based video retrieval system. Recently, Dense Trajectory Features has attracted much attention due to its effective encoding for motion features. On the other hand, beside the Gradient Image, ColorSIFT provide an option to extract the apparent model of interest targets. In order to develop a high-performing system, in this paper, we approach the fusion methods of appearance feature ColorSIFT and motion feature MBH, wich are following Dense Trajectory. After that, the model BoW (Bag of Words) is utilized to aggregate a feature set into a single vector. And then, SVM classifier is used to recognize them. Our approach gives the performance of early fusion classification of 95.1% that outperforms other conventional features under the UCF11 dataset.