Real-time lane tracking using Rao-Blackwellized particle filter

Journal of Real-Time Image Processing

Volumn 11, Issue 1, 2016, Pages 179-191

  Nieto, Marcos ^a   Cortés, Andoni ^a   Otaegui, Oihana ^a   Arróspide, Jon ^b   Salgado, Luis ^b,c  

^a VICOMTECH   (Spain)

^b UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^c UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ADVANCED DRIVER ASSISTANCE SYSTEMS; AUTOMOBILE DRIVERS; EMBEDDED SYSTEMS; MONTE CARLO METHODS; PROGRAM PROCESSORS; ROAD AND STREET MARKINGS; ROADS AND STREETS; TRANSPORTATION; VEHICLE PERFORMANCE;

EFFICIENT DESIGNS; ILLUMINATION CONDITIONS; LANE DEPARTURE WARNING; LANE TRACKING; PROCESSING TIME; RAO-BLACKWELLIZED PARTICLE FILTER; SINGLE CAMERAS; TRAFFIC DENSITIES;

REAL TIME SYSTEMS;

EID: 84955745095     PISSN: 18618200     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11554-012-0315-0     Document Type: Article

References (16)

1. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/nonGaussian Bayesian tracking. IEEE. Trans. Signal. Process. 50(2), 174–188 (2002)
2. Borkar, A., Hayes, M., Smith, M.T.: Lane detection and tracking using a layered approach. In: Proceedings of Advanced Concepts for Intelligent Vision Systems, pp. 474–484. (2009)
3. Borkar, A., Hayes, M., Smith, M.T.: A new multi-camera approach for lane departure warning. In: Proceedings of Advanced Concepts for Intelligent Vision Systems, pp. 59–69. (2011)
4. Chiu, C.-C., Chung, M.-L., Chen, W.-C.: Real-time front vehicle detection algorithm for an asynchronous binocular system. J. Inf. Sci. Eng. 26(3), 735–752 (2010)
5. Corridori, C., Zanin, M.: High curvature two-clothoid road model estimation. In: IEEE Proceedings of Intelligent Transportation Systems Conference, pp. 630–636. (2004)
6. Danescu, R., Nedevschi, S., Meinecke, M.-M., To, T.-B.: A stereovision-based probabilistic lane tracker for difficult road scenarios. In: IEEE Proceedings of Intelligent Vehicles Symposium, pp. 536–541. (2008)
7. del Blanco, C.R., Jaureguizar, F., García, N.: An advanced Bayesian model for the visual tracking of multiple interacting objects. EURASIP J. Adv. Signal Process. 130 (2011)
8. Dickmanns, E.D., Graefe, V.: Dynamic monocular machine vision. MachineVision Appl. 1(4), 223–240 (1988)
9. Doucet, A., Gordon, N.J., Krishnamurthy, V.: Particle filters for state estimation of jump Markov linear systems. IEEE Trans. Signal Process. 49(3), 613–624 (2001)
10. Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1805–1819 (2005)
11. Kim, Z.: Robust lane detection and tracking in challenging scenarios. IEEE. Trans. Intell. Transp. Syst. 9(1), 16–26 (2008)
12. McCall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: survey, system and evaluation. IEEE. Trans. Intell. Transp. Syst. 7(1), 20–37 (2006)
13. Nieto, M., Arróspide, J., Salgado, L.: Road environment modeling using robust perspective analysis and recursive Bayesian segmentation. Mach. Vis. Appl. 22(6), 927–945 (2011)
14. Smal, I., Niessen, W., Meijering, E.: Advanced particle filtering for multiple object tracking in dynamic fluorescence microscopy images. In: IEEE Proceedings of International Symposium on Biomedical Imaging, pp. 1048–1051. (2007)
15. Southall, B., Taylor, C.J.: Stochastic road shape estimation. In: Proceedings of International Conference on Computer Vision, vol. 1, pp. 205–212. (2001)
16. Wang, Y., Teoh, E.K., Shen, D.: Lane detection and tracking using B-snakes. Image. Vis. Comput. 22, 269–289 (2004)