Combining Statistical and Semantic Features for Trajectory Point Classification

Abstract

AbstractTrajectory point classification can be described as a supervised sequence labeling problem, in which a model is trained by labeling data to predict the category of unknown points and identify key events in the trajectory. Due to the difficulty of labeling trajectory point, a large amount of trajectory data is either unlabeled or labeled in an imbalanced way. To make matters worse, traditional trajectory point classification methods are generally constrained to utilize the statistical features of the labeled data and the semantic features as well as the large amount of unlabeled data have not been well studied yet. For this reason, the performance of traditional trajectory point classification methods is far from satisfactory. To solve this problem, we transfer existing language model knowledge to construct the semantic features and construct a trajectory point classification model by combining both the motion features and semantic features. The simulation results show that, compared with the traditional methods, our method has improved the accuracy of trajectory point classification by three and seven percentage points in the classification of circular and turning movements respectively.KeywordsTrajectory point classificationSequence labelingMotion features and semantic featuresLanguage model