Three-step-ahead prediction for object tracking

In this paper, a three-step-ahead prediction method is introduced using chaotic dynamics for state estimation in object tracking. The nonlinear movement of an object is embedded into a low-dimensional state space to utilize the short-term predictions of chaotic systems. The computational architecture of the method is structured as follows. A pseudo-orbit methodology is presented to embed the high dimensional observations of non-linear movement into the pseudo trajectory in the state space with chaotic characteristics. After the Grey theory is applied into the pseudo trajectory in order to reduce the dimension of trajectory, the fractal method is used for three state predictions of the object's movement. For state correction, ensemble members are used to select the best state based on the likelihood function of the color model of candidates. In order to evaluate the efficiency of the chaotic tracker, we compare the chaotic tracker against tracking by detection and stochastic methods. The numerical results demonstrate that the method predicts the target in full occlusions and abrupt motion with a high level of accuracy. Thus, the chaos-based method for making target prediction is vastly superior to existing trackers. The tracker can localize small targets in video sequences accurately. The proposed algorithm is about two times faster than the particle filter method while the error of the particle filter is more than the error of the proposed tracker. The limitations of the proposed method are also illustrated in clutter background and complex scene.