The understanding and description of object behav iors is a hot topic in computer vision. Trajectory analysis is one of the basic problems in behavior understanding, and the learning of trajectory patterns that can be used to detect anomalies and predict object trajectories is an interesting and important problem in tra jectory analysis. In this paper, we present a hierarchical self-orga nizing neural network model and its application to the learning of trajectory distribution patterns for event recognition. The distribu tion patterns of trajectories are learnt using a hierarchical self-or ganizing neural network. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Compared with the existing neural network structures that are used to learn patterns of trajectories, our network structure has smaller scale and faster learning speed, and is thus more effective. Experimental results using two different sets of data demonstrate the accuracy and speed of our hierarchical self-organizing neural network in learning the distribution patterns of object trajectories. Index Terms-Hierarchical self-organizing neural network, tra jectory analysis and learning, anomaly detection, behavior predic tion.
Hu et al. REF proposed a hierarchical self-organizing neural network model to learn the motion patterns of single moving object.
A hierarchical self-organizing approach for learning the patterns of motion trajectories.
IEEE transactions on neural networks
IEEE transactions on neural networks
Computer Science Medicine