Detecting Abnormal Vehicular Dynamics at Intersections Based on an Unsupervised Learning Approach and a Stochastic Model

Sensors

Hugo Jiménez-Hernández²  Jose-Joel González-Barbosa¹ 

[1] Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada. Cerro Blanco No. 141. Col. Colinas del Cimatario, Querétaro, Mexico; E-Mail:;Centro de Ingeniería y Desarrollo Industrial, Av. Pie de la Cuesta No. 702, Desarrollo San Pablo, Querétaro, Mexico

关键词: abnormal activities detection;    unsupervised learning;    long binary strings;   

DOI  :  10.3390/s100807576

来源: mdpi

【 摘 要 】

This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings. In the second stage, using sequences of the recorded states, a stochastic graph model based on a Markovian approach is built. A behavior is labeled abnormal when current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. The approach is tested with several sequences of images acquired from a vehicular intersection where the traffic flow and duration used in connection with the traffic lights are continuously changed throughout the day. Finally, the low complexity and the flexibility of the approach make it reliable for use in real time systems.

【 授权许可】

CC BY   
© 2010 by the authors; licensee MDPI, Basel, Switzerland.