Abstract
The recognition of individual object instances in single monocular images is still an incompletely solved task. In this work, we propose a new approach for detecting and separating vehicles in the context of autonomous driving. Our method uses the fully convolutional network (FCN) for semantic labeling and for estimating the boundary of each vehicle. Even though a contour is in general a one pixel wide structure which cannot be directly learned by a CNN, our network addresses this by providing areas around the contours. Based on these areas, we separate the individual vehicle instances. In our experiments, we show on two challenging datasets (Cityscapes and KITTI) that we achieve state-of-the-art performance, despite the usage of a subsampling rate of two. Our approach even outperforms all recent works w.r.t. several rating scores.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 1097–1105 (2012)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. (IJCV) 88, 303–338 (2010)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. Trans. Pattern Anal. Mach. Intell. (PAMI) (2016). http://ieeexplore.ieee.org/abstract/document/7478072/
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361 (2012)
Tighe, J., Niethammer, M., Lazebnik, S.: Scene parsing with object instances and occlusion ordering. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3748–3755 (2014)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. Trans. Pattern Anal. Mach. Intell. (PAMI) 32, 1627–1645 (2010)
Ladický, Ľ., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.S.: What, where and how many? combining object detectors and CRFs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 424–437. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_31
He, X., Gould, S.: An exemplar-based CRF for multi-instance object segmentation. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 296–303 (2014)
Zhang, Z., Fidler, S., Urtasun, R.: Instance-level segmentation for autonomous driving with deep densely connected MRFs. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zhang, Z., Schwing, A.G., Fidler, S., Urtasun, R.: Monocular object instance segmentation and depth ordering with CNNs. In: International Conference on Computer Vision (ICCV), pp. 2614–2622 (2015)
Uhrig, J., Cordts, M., Franke, U., Brox, T.: Pixel-level encoding and depth layering for instance-level semantic labeling. In: Rosenhahn, B., Andres, B. (eds.) GCPR 2016. LNCS, vol. 9796, pp. 14–25. Springer, Cham (2016). doi:10.1007/978-3-319-45886-1_2
Ghiasi, G., Fowlkes, C.C.: Laplacian pyramid reconstruction and refinement for semantic segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 519–534. Springer, Cham (2016). doi:10.1007/978-3-319-46487-9_32
Lin, G., Shen, C., van den Hengel, A., Reid, I.: Efficient piecewise training of deep structured models for semantic segmentation. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.: Conditional random fields as recurrent neural networks. In: International Conference on Computer Vision (ICCV), pp. 1529–1537 (2015)
Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: Learning rich features from RGB-D images for object detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 345–360. Springer, Cham (2014). doi:10.1007/978-3-319-10584-0_23
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 297–312. Springer, Cham (2014). doi:10.1007/978-3-319-10584-0_20
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. Trans. Pattern Anal. Mach. Intell. (PAMI) 38, 142–158 (2016)
Wu, Z., Shen, C., van den Hengel, A.: Bridging Category-level and Instance-level Semantic Image Segmentation. arXiv:1605.06885 [cs.CV] (2016)
Ren, M., Zemel, R.S.: End-to-End Instance Segmentation and Counting with Recurrent Attention. arXiv:1605.09410 [cs.LG] (2016)
Liang, X., Wei, Y., Shen, X., Yang, J., Lin, L., Yan, S.: Proposal-free network for instance-level object segmentation. arXiv:1509.02636 [cs.CV] (2015)
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. Trans. Pattern Anal. Mach. Intell. (PAMI) 26, 530–549 (2004)
Mairal, J., Leordeanu, M., Bach, F., Hebert, M., Ponce, J.: Discriminative sparse image models for class-specific edge detection and image interpretation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 43–56. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88690-7_4
Dollar, P., Tu, Z., Belongie, S.: Supervised learning of edges and object boundaries. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1964–1971 (2006)
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: International Conference on Computer Vision (ICCV), pp. 991–998 (2011)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. Trans. Pattern Anal. Mach. Intell. (PAMI) 33, 898–916 (2011)
Gupta, S., Arbeláez, P., Girshick, R., Malik, J.: Indoor scene understanding with RGB-D images: bottom-up segmentation, object detection and semantic segmentation. Int. J. Comput. Vis. (IJCV) 112, 133–149 (2015)
Kokkinos, I.: Pushing the boundaries of boundary detection using deep learning. In: International Conference on Learning Representations (ICLR) (2016)
Rupprecht, C., Huaroc, E., Baust, M., Navab, N.: Deep Active Contours. arXiv:1607.05074 [cs.CV] (2016)
Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3982–3991 (2015)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arxiv:1408.5093 (2014)
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
van den Brand, J., Ochs, M., Mester, R. (2017). Instance-Level Segmentation of Vehicles by Deep Contours. 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_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-54407-6_32
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)