Gyro-aided image-based tracking using mutual information optimization and user inputs

In this paper, a template tracking algorithm is presented that uses mutual information (MI) criteria for template matching and gyroscope information to predict rotation between two camera images. The tracking algorithm can also take an user input for template selection and update. The tracking algorithm uses Hu moments that are invariant to 2D rotation, translation and scaling to validate the tracker. Homography is used to represent template warping parameters. The algorithm is aided with gyroscope measurements to estimate the camera motion information which helps to improve the initial guess of the warping condition. Template selection using the user´s input is based on properties of the target, such as its location in the frame. The user driven strategy makes the tracker capable of tracking different objects of interest and might reduce the computational burden for template localization. The tracking algorithm presented in this paper shows significant improvements over recently developed gyro-aided Kanade-Lucas-Tomasi (KLT) tracker and the MI-only tracker in the case of multi-modal images and rapid camera motion.