基于双向非线性学习的轨迹跟踪和识别

目标的运动轨迹是跟踪和识别目标行为的重要特征之一,在视觉跟踪等领域得到了广泛的应用.然而,由于轨迹数据具有高维和非线性等特点,因而直接建模目标的运动轨迹比较困难.为此,引入一种称为自编码(autoencoder)的双向深层神经网络,并结合粒子滤波提出一种轨迹跟踪识别算法.首先,自编码网络按照一定的学习规则将高维轨迹嵌人到二维平面上,通过该网络的逆向映射得到轨迹的生成模型,由轨迹生成模型可得到一系列可行性轨迹.跟踪过程中,每时刻粒子滤波器的粒子便从这些可行性轨迹中进行抽样,并利用颜色似然函数对抽取的粒子进行加权以及再抽样从而实现对目标状态的估计,最后在二维平面中利用"最小距离分类器"对跟踪轨迹进行识别.特别地,自编码网络提供了高维轨迹空间和低维嵌套结构的双向映射,有效解决了大多数非线性降维方法(例如局部线性嵌入算法(LLE)和等度规映射(ISOMAP))所不具备的逆向映射问题.跟踪和识别手写数字实验表明所提出的方法能在复杂背景下精确跟踪目标并正确识别目标轨迹.

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.