Dynamic Bayesian Networks for Vehicle Classification in Video

• Published in: IEEE transactions on industrial informatics Vol. 8; no. 1; pp. 100 - 109
• Main Authors: Kafai, M., Bhanu, B.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayesian analysis; Bayesian methods; Classification; Dynamics; Feature extraction; hybrid dynamic Bayesian network (HDBN); Image color analysis; Image edge detection; Licenses; Mathematical analysis; Networks; Shape; Studies; Trucks; Vectors (mathematics); Vehicles
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2011.2173203

Vehicle classification has evolved into a significant subject of study due to its importance in autonomous navigation, traffic analysis, surveillance and security systems, and transportation management. While numerous approaches have been introduced for this purpose, no specific study has been conducted to provide a robust and complete video-based vehicle classification system based on the rear-side view where the camera's field of view is directly behind the vehicle. In this paper, we present a stochastic multiclass vehicle classification system which classifies a vehicle (given its direct rear-side view) into one of four classes: sedan, pickup truck, SUV/minivan, and unknown. A feature set of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector. The feature vector is then processed by a hybrid dynamic Bayesian network to classify each vehicle. Results are shown on a database of 169 videos for four classes.