2026-04-17T12:49:14Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/163712024-06-18T12:17:41Zcom_2072_452955com_2072_2054col_2072_453069

00925njm 22002777a 4500 dc Guo, Yuejun author Xu, Qing author Li, Peng author Sbert, Mateu author Yang, Yu author 2017-06-30 In this paper, we propose to improve trajectory shape analysis by explicitly considering the speed attribute of trajectory data, and to successfully achieve anomaly detection. The shape of object motion trajectory is modeled using Kernel Density Estimation (KDE), making use of both the angle attribute of the trajectory and the speed of the moving object. An unsupervised clustering algorithm, based on the Information Bottleneck (IB) method, is employed for trajectory learning to obtain an adaptive number of trajectory clusters through maximizing the Mutual Information (MI) between the clustering result and a feature set of the trajectory data. Furthermore, we propose to effectively enhance the performance of IB by taking into account the clustering quality in each iteration of the clustering procedure. The trajectories are determined as either abnormal (infrequently observed) or normal by a measure based on Shannon entropy. Extensive tests on real-world and synthetic data show that the proposed technique behaves very well and outperforms the state-of-the-art methods This work has been funded by Natural Science Foundation of China (61471261, 61179067, U1333110) and Spanish ministry MINECO (TIN2016-75866-C3-3-R). First author acknowledges the support from Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya and the European Social Fund http://hdl.handle.net/10256/16371 Kernel, Funcions de Kernel functions Entropia (Teoria de la informació) Entropy (Information theory) Rutes aleatòries (Matemàtica) Random walks Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools