Vehicle speeding early warning model using frame feature detection and HMM

The techniques of digital image analysis, features detection, and Hidden Markov Model are employed to develop a vehicle speed prediction system, which used to find the trend of the speed changed. The proposed vehicle speed prediction model is used to set up a vehicle speeding early warning model. The proposed vehicle speed early warning system includes the vehicle speed computation and prediction model. All the data source of this study is obtained by the special design vehicular digital video recorder device that includes well defined driving data format. The data of digital video recorder, which represented the driving state data of the vehicle the speed is included, are analyzed to set up the speed computation and prediction model. The proposed approaches can closely match the vehicle speed and its concurrent video frame. The results of this study can also provide other vehicular computer vision techniques to reduce the processing time along with vehicle's speed.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Sunglok Choi,et al.  Performance Evaluation of RANSAC Family , 2009, BMVC.

[4]  Ming-Shi Wang,et al.  An Implementation of a Vehicular Digital Video Recorder System , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[5]  Horst Bunke,et al.  Hidden Markov models: applications in computer vision , 2001 .

[6]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[7]  P. Wolf,et al.  Elements of Photogrammetry(with Applications in GIS) , 2000 .

[8]  Murat Gevrekci,et al.  Illumination robust interest point detection , 2009, Comput. Vis. Image Underst..

[9]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[10]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[11]  Andrew Blake,et al.  Fusion Moves for Markov Random Field Optimization , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[13]  W. Zucchini,et al.  Hidden Markov Models for Time Series: An Introduction Using R , 2009 .

[14]  Tak Kuen Siu,et al.  Markov Chains: Models, Algorithms and Applications , 2006 .