Edge enhanced SIFT for moving object detection
This paper is to report our study on the moving object detection from surveillance images. For motion detection, some existed methods are used to find specific features between images and then to define the moving speeds of objects. However, human-created features may be difficult to define and to acquire especially when the objects are unknown. In this paper, the scale-invariant feature transform (SIFT) method is adopted to define features for motion detection. In the image, SIFT can be used to catch the properties of scale and rotation invariant. Even if the foreground target is partially obscured and the image is taken in a different angle and distance, SIFT can still have nice matching performances. However, when applied in detecting moving objects, SIFT does not work well due to finding incorrect features in the match. In this study, an enhanced edge detection method is proposed for Laplace of Gaussian (LoG) based SIFT. From the simulation results, it is evident that our proposed method can find more feature matching in the video image.