© The Institution of Engineering and Technology
Forward collision avoidance systems have shown to be a particularly effective crash-avoidance technology. Multi-vehicle tracking capabilities play an important role in the real-world performance and effectiveness of such systems. In order to effectively and accurately track vehicles in a moving platform and in complicated road environments, the authors proposed a multi-vehicle tracking algorithm based on an improved particle filter. First, the authors used a vehicle disappearance detection and handling mechanism based on the normalised area of the minimum circumscribed rectangle of particle distributions. This mechanism is used to verify whether a new target is a vehicle and can also handle the vehicle exit during the tracking phase. Next, an improved particle filter-based framework, which includes a new process dynamical distribution, allowed for multi-vehicle tracking capabilities was used for vehicle tracking. Finally, an effective occlusion detection and handling mechanism was used to address the significant occlusion between vehicles. The combination of these added improvements in the algorithm results in the enhancement of the vehicle tracking rate in a variety of challenging conditions. Experimental tests carried out from different datasets show excellent performance in multi-vehicle tracking, in terms of accuracy in complex traffic situations and under different lighting conditions.
References
-
-
1)
-
12. Hess, R., Fern, A.: ‘Discriminatively trained particle filters for complex multi-object tracking’. Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Miami, Florida, USA, June 2009, pp. 240–247.
-
2)
-
B. Leibe ,
K. Schindler ,
N. Cornelis ,
L. Van Gool
.
Coupled object detection and tracking from static cameras and moving vehicles.
IEEE Trans. Pattern Anal. Mach. Intell.
,
10 ,
1683 -
1698
-
3)
-
2. Arrospide, J., Slgado, L., Nieto, M., Jaureguizar, F.: ‘On-board robust vehicle detection and tracking using adaptive quality evaluation’. Proc. IEEE Int. Conf. on Image Processing, San Diego, California, USA, October 2008, pp. 2008–2012.
-
4)
-
3. Lim, Y.C., Lee, M., Lee, C.H., Kwon, S., Lee, J.H.: ‘Integrated position and motion tracking method for online multi-vehicle tracking-by-detection’, Optical Engineering, 2011, 50, (7), pp. 077203-1–077203-10.
-
5)
-
21. Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J.S., Mostefa, D., Soundararajan, P.: ‘The CLEAR 2006 evaluation’. CLEAR, 2006.
-
6)
-
23. UCSD LISA Datasets: .
-
7)
-
8. Chang, C., Ansari, R., Khokhar, A.: ‘Multiple object tracking with kernel particle filter’. Proc. Int. Conf. on Computer Vision and Pattern Recognition, San Diego, CA, USA, June 2005, pp. 566–573.
-
8)
-
Z. Khan ,
T.R. Balch ,
F. Dellaert
.
MCMC-based particle filtering for tracking a variable number of interacting targets.
IEEE Trans. Pattern Anal. Mach. Intell.
,
11 ,
1805 -
1918
-
9)
-
5. Chan, Y.M., Huang, S.S., Fu, L.C., Hsiao, P.Y., Lo, M.F.: ‘Vehicle detection and tracking under various lighting conditions using a particle filter’, IET Intell. Transp. Syst., 2012, 6, pp. 1–8 (doi: 10.1049/iet-its.2011.0019).
-
10)
-
10. Liu, C.G., Cheng, D.S., Liu, J.F., Huang, J.H., Tang, X.L.: ‘Interactive particle filter based algorithm for tracking multiple objects in videos’, Acta Electron. Sin., 2011, 39, (2), pp. 260–267.
-
11)
-
9. Li, W.H., Zhou, Q., Wang, Y., Zhang, D.C.: ‘Adaptive tracking algorithm based on particle filter-mean shift’, J. Jilin Univ. Inf. Sci. Ed. (China), 2012, 42, (2), pp. 407–411.
-
12)
-
4. Xie, W.H., Xiao, J.S., Yi, B.S., Zhang, Y.Q., Li, M.: ‘A vehicle tracking algorithm based on mean shift and C-V model’, J. Hunan Univ. (Nat. Sci.), 2012, 39, (7), pp. 31–36.
-
13)
-
Z. Sun ,
G. Bebis ,
R. Miller
.
On-road vehicle detection: a review.
IEEE Trans. Pattern Anal. Mach. Intell.
,
5 ,
694 -
711
-
14)
-
7. Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: ‘Online multiperson tracking-by detection from a single, uncalibrated camera’, IEEE Trans. PAMI, 2011, 33, (9), pp. 1820–1833 (doi: 10.1109/TPAMI.2010.232).
-
15)
-
6. Arrospide, J., Salgado, L., Nieto, M.: ‘Multiple object tracking using an automatic variable dimension particle filter’. Proc. IEEE Int. Conf. on Image Processing, Hong Kong, China, September 2010, pp. 49–52.
-
16)
-
15. Liu, C., Wang, G.J., Jiang, F., Lin, X.G.: ‘Online HOG method in pedestrian tracking’, IEICE Trans. Inf. Syst., 2010, E93, (5), pp. 1321–1324 (doi: 10.1587/transinf.E93.D.1321).
-
17)
-
18. Andriyenko, A., Schindler, K., Roth, S.: ‘Discrete-continuous optimization for multi-target tracking’. Proc. Int. Conf. on Computer Vision and Pattern Recognition, Providence, Rhode Island, USA, June 2012, pp. 1926–1933.
-
18)
-
20. Tewodros, A.B., Carlo, S.R.: ‘A bayesian network for online evaluation of sparse features based multi-target tracking’. Pro. Int. Conf. on Image Processing, Orlando, Florida, USA, September 2012, pp. 429–432.
-
19)
-
11. Okuma, K., Taleghani, A., Freitas, N., Little, J.J., Lowe, D.G.: ‘A boosted particle filter: multi-target detection and tracking’. Proc. European Conf. on Computer Vision, Prague, Czech Republic, May 2004, pp. 28–39.
-
20)
-
13. Javed, O., Ali, S., Shah, M.: ‘Online detection and classification of moving objects using progressively improving detectors’. Proc. Int. Conf. on Computer Vision and Pattern Recognition, San Diego, CA, USA, June 2005, pp. 696–701.
-
21)
-
19. Andriyenko, A., Schindler, K.: ‘Multi-target tracking by continuous energy minimization’. Proc. Int. Conf. on Computer Vision and Pattern Recognition, Colorado Springs, USA, June 2011, pp. 1265–1272.
-
22)
-
14. Grabner, H., Bischof, H.: ‘On-line boosting and vision’. Proc. Int. Conf. Computer Vision and Pattern Recognition, New York, NY, USA, June 2006, pp. 260–267.
-
23)
-
24)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2014.0088
Related content
content/journals/10.1049/iet-its.2014.0088
pub_keyword,iet_inspecKeyword,pub_concept
6
6