Application Of Scale Invariant Feature Moving Target Tracking And Detection

With the decline in the cost of the calculation and the popularity of high-precision image capture device, computer vision-based automatic monitoring, human-computer interaction, panorama stitching technology has been widely used. This paper focuses on basic computer vision tracking and detection of moving targets. The main contents as follows.In this paper, first we analyze the scale space theory. Then Introduce the SIFT feature extraction and matching algorithm and the Gaussian pyramid method for scale invariant feature.This thesis presents an object tracking algorithm that combined the optical flow estimation algorithm and the Scale Invariant Feature Transform (SIFT) with a new template update strategy for the change of scene and object. The SIFT feature is local feature which is invariant to the scale and rotation change of the image. The optical flow is a velocity field and the whole feature which represents the change of intensity of the pixel. The SIFT feature satisfies the condition of the optical flow estimation method. The experimental results show that our improved method can be used in partial covering tracking, and can achieve more accurate tracking than the traditional method.Image panorama stitching framework is introduced. Moving target tracking and monitoring approach is used for dealing with the ghosting problems in the panoramic problem. Experimental results show that this method can effectively deal with the ghosting in panorama stitching.