Motion detection in moving background is difficult. Background compensation followed by inter-frame difference and optical flow segmentation can separate object from background. Problems are that the former requiresrobust background estimation and may induce holes, while optical flow estimation is often invalid with noise, changes in illumination and high speed object. Two methods may be invalid especially when illumination changes. A new approach is presented based on feature displacement vector field fuzzy segmentation and OTSU method for motion detection in dynamic scenes without any prior information about object or dynamic scenes. A robust feature correspondences set is obtained by an improved matching strategy of SIFT, and Fuzzy C-means clustering algorithm is used to classify the generated feature displacement vectors. OTSU algorithm and morphological operations are performed for image thresholding, which modifies the convex hull of detected features. At last, moving object region is segmented. Compared with inter-frame difference and optical flow estimation, experiments demonstrate that the proposed method can detect moving object in the condition of noise, illumination change and object moving at a high speed.