Classification of Trajectories Using Category Maps and U-Matrix to Predict Interests Used for Event Sites

This paper presents a method for clas- sification and recognition of behavior patterns based on interest from human trajectories at an event site. Our method creates models using Hidden Markov Models (HMMs) for each human trajectory quantized using One-Dimensional Self-Organizing Maps (1D- SOMs). Subsequently, we apply Two-Dimensional SOMs (2D-SOMs) for unsupervised classification of behavior patterns from features according to the dis- tance between models. Furthermore, we use a Unified distance Matrix (U-Matrix) for visualizing category boundaries based on the Euclidean distance between weights of 2D-SOMs. Our method extracts typical be- havior patterns and specific behavior patterns based on interest as ascertained using questionnaires. Then our method visualizes relations between these patterns. We evaluated our method based on Cross Validation (CV) using only the trajectories of typical behavior patterns. The recognition accuracy improved 9.6% over that of earlier models. We regard our method as useful to estimate interest from behavior patterns at an event site.