Current research on road tracking is mostly based on the visual perception of road boundaries. In this paper, we propose a novel and general road tracker based on optical flow computation, which can be applied to most of road environments including the case of a lack of lane markings or road boundaries. When the heading direction of the vehicle and the road direction are identical, the focus of expansion (FOE) coincides with the road vanishing point (RVP). This is an important foundation for the subsequent heading direction departure decision. By comparing the relative positions of the estimated FOE and RVP, we can learn the traveling state of the vehicle. The experimental results show that the proposed tracker is a simple and efficient road tracking method.
SoteloMARodriguezFJMagdalenaLet al. A color vision-based lane tracking system for autonomous driving on unmarked roads. Auton Robot2004; 16: 95–116.
2.
LiZDaiBHeH. A novel fast segmentation method of unstructured roads. In: Proceedings of IEEE international conference on vehicular electronics and safety (ICVES 2006), Shanghai, China, 13–15 December 2006, pp.53–56. New York: IEEE.
3.
AlvarezJMLopezABaldrichR.Illuminant-invariant model-based road segmentation. In: Proceedings of 2008 IEEE intelligent vehicles symposium, Eindhoven, the Netherlands, 4–6 June 2008, pp.1175–1180. New York: IEEE.
4.
RasmussenC. Grouping dominant orientations for ill-structured road following. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition (CVPR 2004), Washington, DC, 27 June–2 July 2004, vol. 1, pp.1–8. New York: IEEE.
5.
KongHAudibertJYPonceJ.General road detection from a single image. IEEE T Image Process2010; 19: 2211–2220.
6.
WangJJiZSuYT.Unstructured road detection using hybrid features. In: Proceedings of 2009 international conference on machine learning and cybernetics, Hebei, China, 12–15 July 2009, vol. 1, pp.482–486. New York: IEEE.
7.
ZhouSGongJXiongGet al. Road detection using support vector machine based on online learning and evaluation. In: Proceedings of IEEE intelligent vehicles symposium (IV), San Diego, CA, 21–24 June 2010, pp.256–261. New York: IEEE.
8.
MalikUAAhmedSUKunwarF. A self-organizing neural scheme for road detection in varied environments. In: Proceedings of the 2011 international joint conference on neural networks (IJCNN), San Jose, CA, 31 July–5 August 2011, pp.3049–3054. New York: IEEE.
9.
ShinzatoPYGrassiVJrOsorioFSet al. Fast visual road recognition and horizon detection using multiple artificial neural networks. In: Proceedings of IEEE intelligent vehicles symposium (IV), Alcala de Henares, Spain, 3–7 June 2012, pp.1090–1095. New York: IEEE.
10.
ZhangGZhengNCuiCet al. An efficient road detection method in noisy urban environment. In: Proceedings of IEEE intelligent vehicles symposium, Xi’an, China, 3–5 June 2009, pp.556–561. New York: IEEE.
11.
WangYTeohEKShenD.Lane detection and tracking using b-snake. Image Vision Comput2004; 22: 269–280.
12.
YenikayaSYenikayaGDüvenE.Keeping the vehicle on the road: a survey on on-road lane detection systems. ACM Comput Surv2013; 46: 2.
13.
FodaSGDawoudAK. Highway lane boundary determination for autonomous navigation. In: Proceedings of IEEE pacific-rim conference on communications, computers and signal processing (PACRIM 2001), Victoria, BC, 26–28 August 2001, vol. 2, pp.698–702. New York: IEEE.
14.
WuBFLinCT. Robust lane detection and tracking for driving assistance systems. In: Proceedings of IEEE international conference on systems, man and cybernetics (ISIC 2007), Montreal, QC, 7–10 October 2007, pp.3848–3853. New York: IEEE.
15.
TsaiLWHsiehJWChuangCHet al. Lane detection using directional random walks. In: Proceedings of IEEE intelligent vehicles symposium, Eindhoven, the Netherlands, 4–6 June 2008, pp.303–306. New York: IEEE.
16.
LiuJLiZZhangHet al. A vision-based road recognition algorithm. In: Proceedings of the 3rd IEEE conference on industrial electronics and applications (ICIEA 2008), Singapore, 3–5 June 2008, pp.284–287. New York: IEEE.
17.
RotaruCGrafTZhangJ.Extracting road features from color images using a cognitive approach. In: Proceedings of IEEE intelligent vehicles symposium, Parma, Italy, 14–17 June 2004, pp.298–303. New York: IEEE.
18.
WuPCChangCYLinCH.Lane-mark extraction for automobiles under complex conditions. Pattern Recogn2014; 47: 2756–2767.
19.
ShinBSTaoJKletteR.A superparticle filter for lane detection. Pattern Recogn2014; 48: 3333–3345.
20.
MoghadamPStarzykJAWijesomaWS.Fast vanishing-point detection in unstructured environments. IEEE T Image Process2012; 21: 425–430.
21.
LucasBDKanadeT.An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence (IJCAI ‘81), Vancouver, BC, 24–28 August 1981, vol. 81, pp.674–679. New York: ACM.
TuZPoppeRVeltkampRC.Weighted local intensity fusion method for variational optical flow estimation. Pattern Recogn2016; 50: 223–232.
24.
TuZVan Der AaNVan GemerenCet al. A combined postfiltering method to improve accuracy of variational optical flow estimation. Pattern Recogn2014; 47: 1926–1940.
25.
AmiazTLubetzkyEKiryatiN.Coarse to over-fine optical flow estimation. Pattern Recogn2007; 40: 2496–2503.
26.
ParkSCLeeHSLeeSW.Qualitative estimation of camera motion parameters from the linear composition of optical flow. Pattern Recogn2004; 37: 767–779.
27.
SunDRothSBlackMJ. Secrets of optical flow estimation and their principles. In: Proceedings of 2010 IEEE conference on computer vision and pattern recognition (CVPR), San Francisco, CA, 13–18 June 2010, pp.2432–2439. New York: IEEE.
28.
BouguetJY.Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corp2001; 5: 4.
29.
BroxTBruhnAPapenbergNet al. High accuracy optical flow estimation based on a theory for warping. In: PajdlaTMatasJ (eds) Computer vision (ECCV 2004). Berlin: Springer, 2004, pp.25–36.
30.
BroxTMalikJ.Large displacement optical flow: descriptor matching in variational motion estimation. IEEE T Pattern Anal2011; 33: 500–513.
31.
BruhnAWeickertJSchnörrC.Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. Int J Comput Vision2005; 61: 211–231.
32.
LiuC.Beyond pixels: exploring new representations and applications for motion analysis. PhD Thesis, Massachusetts Institute of Technology, USA, 2009.
