Abstract
To robustly detect people and vehicle on the road in a video sequence is a challenging problem. Most researches focus on detecting or tracking of specific targets only. On the contrary, instead of detecting vehicle or pedestrian individually, an integration framework combining the geometric information is proposed. The camera’s pitch angle is estimated with a novel vanishing line estimator. Not only detecting the vanishing point using line intersection approach, but also the object information from tracker are considered. Specifically, the detected vehicle or pedestrian will cast votes for the hypothesized horizon line. The vanishing line can be estimated even when the scenes are cluttered or crowded, and thus the geometric information can be estimated under challenging circumstance. In turn, such information of scene can help the system refine our detection results through Bayes’ network. Finally, to verify the performance of the system, comprehensive experiments have been conducted with the KITTI dataset. It is quite promising that the state-of-the-art detector, in our case, Regionlet detector, can be improved.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hoiem, D., Efros, A., Hebert, M.: Putting objects in perspective. Int. J. Comput. Vision 80, 3–15 (2008)
Nieto, M., Laborda, J.A., Salgado, L.: Road environment modeling using robust perspective analysis and recursive bayesian segmentation. Mach. Vis. Appli. 22, 927–945 (2011)
Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.: 3D traffic scene understanding from movable platforms. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1012–1025 (2014)
Richardson, E., Peleg, S., Werman, M.: Scene geometry from moving objects. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 13–18 (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Patter Recognition, vol. 1, pp. 886–893 (2005)
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 743–761 (2012)
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: A mobile vision system for robust multi-person tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Wojek, C., Roth, S., Schindler, K., Schiele, B.: Monocular 3D scene modeling and inference: understanding multi-object traffic scenes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 467–481. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_34
Sudowe, P., Leibe, B.: Efficient use of geometric constraints for sliding-window object detection in video. In: Crowley, J.L., Draper, B.A., Thonnat, M. (eds.) ICVS 2011. LNCS, vol. 6962, pp. 11–20. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23968-7_2
Moghadam, P., Starzyk, J.A., Wijesoma, W.S.: Fast vanishing-point detection in unstructured environments. IEEE Trans. Image Process. 21, 425–430 (2012)
Geiger, A., Wojek, C., Urtasun, R.: Joint 3D estimation of objects and scene layout. In: Advances in Neural Information Processing Systems, pp. 1467–1475 (2011)
Lezama, J., Grompone von Gioi, R., Randall, G., Morel, J.M.: Finding vanishing points via point alignments in image primal and dual domains. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 509–515 (2014)
Yang, W., Luo, X., Fang, B., Zhang, D., Tang, Y.Y.: Fast and accurate vanishing point detection in complex scenes. In: IEEE Conference on Intelligent Transportation Systems, pp. 93–98 (2014)
Wildenauer, H., Hanbury, A.: Robust camera self-calibration from monocular images of manhattan worlds. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2831–2838 (2012)
Nieto, M., Salgado, L.: Real-time robust estimation of vanishing points through nonlinear optimization. In: SPIE Photonics Europe, pp. 772402–772402-14. International Society for Optics and Photonics (2010)
Quan, L., Mohr, R.: Determining perspective structures using hierarchical hough transform. Pattern Recogn. Lett. 9, 279–286 (1989)
Dubska, M., Herout, A., Havel, J.: Pclines - line detection using parallel coordinates. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1489–1494 (2011)
Vermaak, J., Doucet, A., Perez, P.: Maintaining multimodality through mixture tracking. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1110–1116 (2003)
Lanz, O.: Approximate bayesian multibody tracking. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1436–1449 (2006)
Kuo, C.H., Huang, C., Nevatia, R.: Multi-target tracking by on-line learned discriminative appearance models. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 685–692 (2010)
Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Van Gool, L.: Online multiperson tracking-by-detection from a single, uncalibrated camera. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1820–1833 (2011)
Leibe, B., Schindler, K., Cornelis, N., Van Gool, L.: Coupled object detection and tracking from static cameras and moving vehicles. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1683–1698 (2008)
Milan, A., Roth, S., Schindler, K.: Continuous energy minimization for multitarget tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36, 58–72 (2014)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20, 226–239 (1998)
Bota, S., Nedesvchi, S.: Multi-feature walking pedestrians detection for driving assistance systems. IET Intel. Trans. Syst. 2, 92–104 (2008)
Isard, M., Blake, A.: Condensation-conditional density propagation for visual tracking. Int. J. Comput. Vision 29, 5–28 (1998)
Matas, J., Galambos, C., Kittler, J.: Robust detection of lines using the progressive probabilistic hough transform. Comput. Vis. Image Underst. 78, 119–137 (2000)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Wang, X., Yang, M., Zhu, S., Lin, Y.: Regionlets for generic object detection. In: IEEE International Conference on Computer Vision, pp. 17–24 (2013)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361 (2012)
Xiang, Y., Alahi, A., Savarese, S.: Learning to track: Online multi-object tracking by decision making. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4705–4713 (2015)
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. J. Image Video Process. 2008, 1–10 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chan, YM., Fu, LC., Hsiao, PY., Huang, SS. (2017). Pedestrian and Vehicle Detection and Tracking with Object-Driven Vanishing Line Estimation. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-54407-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54406-9
Online ISBN: 978-3-319-54407-6
eBook Packages: Computer ScienceComputer Science (R0)