轨迹分布模式学习的层次自组织神经网络方法

提出一个层次自组织神经网络模型，并将其应用于基于事件识别的轨迹分布模式学习中。该文利用神经元的侧向连接将神经元连成若干条线，每条线对应一个“内部网”。对应于层次神经网络模型，建立了两个领域，即神经元领域和“内部网”领域，两个领域内的神经元都要不同程度地改变权值，从而完成运动轨迹分布模式的学习。还给出了利用轨迹分布模式检测出局部可能的异常现象、检测整个运动轨迹所表示的事件是否为异常事件和目标行为预测的方法。实验进一步说明了该方法的可行性和有效性。

A Hierarchical Self-Organizing Approach for Learning the Patterns of Motion Trajectories

This paper presents a hierarchical self organizing neural network model and its application in the learning of the trajectory distribution patterns for event recognition. By linking the side neurons, some lines are formed. Each line becomes an internal net of the hierarchical self organizing neural network. Corresponding to the hierarchical self organizing neural network model, the authors define two neighborhoods, namely the neuron neighborhood and the internal net neighborhood. The neurons in both neighborhoods will update their weights to different extent. In this way, the trajectory distribution patterns can be learned. Using the learned patterns, the authors consider both local and global anomaly detection as well as object behavior prediction. Experimental results demonstrate the effectiveness of the approach to trajectory analysis.