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Tracking Objects On Image Sequences With Unscented Kalman Filter and Dynamic Neural Networks

Tracking objects on image sequences is a problem that has been studied by several authors using the Kalman filter as a position and orientation estimator, this approach is actually used in different applications such as tracking vehicles in traffic control system, closed loop control for robotic positioning systems and orientation tracking of surgical devices using a camera to determine the position in the task space. In order to make the estimations of the position of an object in an image sequence, it is necessary to compute the states dynamics equations that define the object trajectory according to an “a priori” behavior of the dynamic equations and these values are used to estimate the future position in the image frame. These state transition equations are defined by the knowledge of the system dynamics; therefore, they can be represented by the respective spatial transformations. In this paper a different approximation is proposed representing the state transition equations using a dynamic neural network that is used later by the Kalman filter estimator to compute the future states that estimate the position in the image.