Trajectory Tracking and Recognition Using Bi-Directional Nonlinear Learning

Object trajectory is one of the most important cues for tracking and behavior recognition and can be widely applied to numerous such as visual surveillance and guidance. However, it is a difficult problem to directly model spatio-temporal variations of trajectories due to their high dimensionality and nonlinearity. This paper proposes a novel trajectory tracking and recognition algorithm by combining a bi-directional deep neural network called "autoencoder" into a particle filter. First, the "autoencoder" network embeds the high-dimensional trajectories in a two-dimensional plane based on a peculiar training rule and learns a trajectory generative model by the inverse mapping. Then a series of plausible trajectories are generated by the trajectory generative model. In the tracking process, the generated samples from the plausible trajectory set are weighted by the color likelihood and are resampled so as to obtain target state estimation at each time step. Finally the tracking trajectory is recognized by min-distance classification method in the two-dimensional plane. In particular, the "autoencoder" provides such a bi-directional mapping between the high-dimensional trajectory space and the low-dimensional space and is therefore able to overcome the inherited deficiency of most nonlinear dimensionality reduction methods (e.g. LLE and ISOMAP) that do not have an inverse mapping. The experiments on tracking and recognizing handwritten digits show that the proposed algorithm can robustly track and exactly recognize in background clutter.