A Novel Tracking and Recognition Algorithm Using "Continuous Autoencoder" Network

Motion trajectory is one of the most important cues for tracking and behavior recognition and can be widely applied to numerous fields such as visual surveillance and guidance. However, it is a difficult problem to directly model the spatiotemporal variations of trajectories due to their high dimensionality and nonlinearity. In this paper, a novel tracking and trajectory recognition algorithm is proposed by combining a bi-directional deep neural network called "Continuous Autoencoder" into a probabilistic framework. First, the "Continuous Autoencoder" network embeds high-dimensional trajectories in a two-dimensional plane based on a peculiar training rule and learns a trajectory generative model by its inverse mapping. Then a set of plausible trajectories are generated by the trajectory generative model. In the tracking process, the target state at each time step is estimated by combining above plausible trajectory set with particle filter. The trajectory identity is inferred by evaluating the improved Hausdorff distance between the estimated trajectory up to now and the truncated reference trajectories. Furthermore, the trajectory recognition results can provide valuable information for the next tracking. The experiments on tracking and recognizing handwritten digits show that the proposed algorithm can not only robustly track and exactly recognize in background clutter and occlusion, but also realize the track before identification.