Unsupervised obstacle detection in driving environments using deep-learning-based stereovision

Robotics and Autonomous Systems

Volumn 100, Issue , 2018, Pages 287-301

  Dairi, Abdelkader ^a   Harrou, Fouzi ^b   Senouci, Mohamed ^a   Sun, Ying ^b  

^a UNIVERSITY OF ORAN   (Algeria)

^b KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

Autoencoder; DBM; Deep learning; Monitoring; OCSVM; Stereovision

Indexed keywords

AUTOMOBILE DRIVERS; DATABASE SYSTEMS; INTELLIGENT ROBOTS; INTELLIGENT SYSTEMS; LEARNING SYSTEMS; MONITORING; OBSTACLE DETECTORS; SIGNAL ENCODING; STEREO VISION;

AUTO ENCODERS; DEEP BOLTZMANN MACHINES; DIMENSIONALITY REDUCTION; INTELLIGENT TRANSPORTATION SYSTEMS; LOCALIZATION AND TRACKING; OBSTACLE DETECTION SYSTEM; OCSVM; ONE-CLASS SUPPORT VECTOR MACHINES (OCSVM);

DEEP LEARNING;

EID: 85041286281     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2017.11.014     Document Type: Article

References (51)

1. Labayrade, R., Aubert, D., Tarel, J.P., Real time obstacle detection in stereovision on non flat road geometry through “v-disparity” representation. IEEE Intelligent Vehicle Symposium, 2002, Vol. 2, 2002, IEEE, 646–651.
2. Fakhfakh, N., Gruyer, D., Aubert, D., Weighted V-disparity approach for obstacles localization in highway environments. 2013 IEEE Intelligent Vehicles Symposium (IV), 2013, IEEE, 1271–1278.
3. Sun, H., Zou, H., Zhou, S., Wang, C., El-Sheimy, N., Surrounding moving obstacle detection for autonomous driving using stereo vision. Int. J. Adv. Robot. Syst., 10(6), 2013, 261.
4. N. Appiah, N. Bandaru, Obstacle detection using stereo vision for self-driving cars, 2015.
5. Nalpantidis, L., Kragic, D., Kostavelis, I., Gasteratos, A., Theta-disparity: An efficient representation of the 3d scene structure. Intelligent Autonomous Systems 13, 2016, Springer, 795–806.
6. Zhang, X., Song, Y., Yang, Y., Pan, H., Stereo vision based autonomous robot calibration. Robot. Auton. Syst. 93 (2017), 43–51.
7. Woo, J., Kim, N., Vision based obstacle detection and collision risk estimation of an unmanned surface vehicle. 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, 2016, IEEE, 461–465.
8. Del, C., Skaar, S., Cardenas, A., Fehr, L., A sonar approach to obstacle detection for a vision-based autonomous wheelchair. Robot. Auton. Syst. 54:12 (2006), 967–981.
9. Fleischmann, P., Berns, K., A stereo vision based obstacle detection system for agricultural applications. Field and Service Robotics, 2016, Springer, 217–231.
10. Asvadi, A., Premebida, C., Peixoto, P., Nunes, U., 3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes. Robot. Auton. Syst. 83 (2016), 299–311.
11. Yoo, H., Son, J., Ham, B., Sohn, K., Real-time rear obstacle detection using reliable disparity for driver assistance. Expert Syst. Appl. 56 (2016), 186–196.
12. Hu, Z., Uchimura, K., UV-disparity: an efficient algorithm for stereovision based scene analysis. IEEE Intelligent Vehicles Symposium, 2005. Proceedings, 2005, IEEE, 48–54.
13. Dalal, N., Triggs, B., Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. Vol. 1, CVPR 2005, 2005, IEEE, 886–893.
14. Nadav, I., Katz, E., Off-road path and obstacle detection using monocular camera. IEEE International Conference on the Science of Electrical Engineering, 2016, IEEE, 1–5.
15. Broggi, A., Caraffi, C., Fedriga, R.I., Grisleri, P., Obstacle detection with stereo vision for off-road vehicle navigation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, 2005 CVPR Workshops, 2005, IEEE, 65.
16. Yamaguchi, K., Kato, T., Ninomiya, Y., Moving obstacle detection using monocular vision. Intelligent Vehicles Symposium, 2006, IEEE, 288–293.
17. Häne, C., Sattler, T., Pollefeys, M., Obstacle detection for self-driving cars using only monocular cameras and wheel odometry. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, IEEE, 5101–5108.
18. Burlacu, A., Bostaca, S., Hector, I., Herghelegiu, P., Ivanica, G., Moldoveanul, A., Caraiman, S., Obstacle detection in stereo sequences using multiple representations of the disparity map. 2016 20th International Conference on System Theory, Control and Computing (ICSTCC), 2016, IEEE, 854–859.
19. Petković, D., Danesh, A.S., Dadkhah, M., Misaghian, N., Shamshirband, S., Zalnezhad, E., Pavlović, N.D., Adaptive control algorithm of flexible robotic gripper by extreme learning machine. Robot. Comput.-Integr. Manuf. 37 (2016), 170–178.
20. Duguleana, M., Barbuceanu, F.G., Teirelbar, A., Mogan, G., Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning. Robot. Comput.-Integr. Manuf. 28:2 (2012), 132–146.
21. Bengio, Y., LeCun, Y., et al. Scaling learning algorithms towards AI. Large Scale Kernel Mach. 34:5 (2007), 1–41.
22. Dollar, P., Wojek, C., Schiele, B., Perona, P., Pedestrian detection: An evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34:4 (2012), 743–761.
23. Bengio, Y., et al. Learning deep architectures for AI. Found. Trends Mach. Learn. 2:1 (2009), 1–127.
24. Hinton, G.E., Learning multiple layers of representation. Trends Cogn. Sci. 11:10 (2007), 428–434.
25. Nguyen, V.D., Van Nguyen, H., Tran, D.T., Lee, S.J., Jeon, J.W., Learning framework for robust obstacle detection, recognition, and tracking. IEEE Trans. Intell. Transp. Syst., 2016.
26. Ramos, S., Gehrig, S., Pinggera, P., Franke, U., Rother, C., Detecting unexpected obstacles for self-driving cars. Fusing Deep Learn. Geom. Model., 2016 arXiv preprint arXiv:1612.06573.
27. R. Labayrade, D. Aubert, In-vehicle obstacles detection and characterization by stereovision, in: Proceedings of the 1st International Workshop on In-Vehicle Cognitive Computer Vision Systems, Graz, Austria, 2003.
28. Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C., High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognit. 58 (2016), 121–134.
29. Xu, J., Li, H., Zhou, S., An overview of deep generative models. IETE Tech. Rev. 32:2 (2015), 131–139.
30. Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A., Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning, 2008, ACM, 1096–1103.
31. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A., Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11:Dec (2010), 3371–3408.
32. Krizhevsky, A., Hinton, G.E., Using very deep autoencoders for content-based image retrieval. ESANN, 2011.
33. P. Smolensky, Information processing in dynamical systems: Foundations of harmony theory; cu-cs-321-86, 1986.
34. Hinton, G.E., Osindero, S., Teh, Y.-W., A fast learning algorithm for deep belief nets. Neural Comput. 18:7 (2006), 1527–1554.
35. Salakhutdinov, R., Hinton, G., Deep Boltzmann machines. Artificial Intelligence and Statistics, 2009, 448–455.
36. Mohamed, A.-r., Dahl, G.E., Hinton, G., Acoustic modeling using deep belief networks. IEEE Trans. Audio Speech Lang. Process. 20:1 (2012), 14–22.
37. O'Connor, P., Neil, D., Liu, S.-C., Delbruck, T., Pfeiffer, M., Real-time classification and sensor fusion with a spiking deep belief network. Front. Neurosci., 7, 2013.
38. Lee, H., Pham, P., Largman, Y., Ng, A.Y., Unsupervised feature learning for audio classification using convolutional deep belief networks. Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A., (eds.) Advances in Neural Information Processing Systems 22, 2009, Curran Associates, 1096–1104 http://papers.nips.cc/paper/3674-unsupervised-feature-learning-for-audio-classification-using-convolutional-deep-belief-networks.pdf.
39. Kang, S., Qian, X., Meng, H., Multi-distribution deep belief network for speech synthesis. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, IEEE, 8012–8016.
40. P. Liu, S. Han, Z. Meng, Y. Tong, Facial expression recognition via a boosted deep belief network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1805–1812.
41. Salakhutdinov, R., Hinton, G.E., Learning a nonlinear embedding by preserving class neighbourhood structure. AISTATs, Vol.11, 2007.
42. Leng, B., Zhang, X., Yao, M., Xiong, Z., A 3D model recognition mechanism based on deep Boltzmann machines. Neurocomputing 151 (2015), 593–602.
43. Gan, Q., Wu, C., Wang, S., Ji, Q., Posed and spontaneous facial expression differentiation using deep Boltzmann machines. 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), 2015, IEEE, 643–648.
44. Salakhutdinov, R., Larochelle, H., Efficient learning of deep Boltzmann machines. AISTATs, Vol. 9, 2010, 693–700.
45. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C., Estimating the support of a high-dimensional distribution. Neural Comput. 13:7 (2001), 1443–1471.
46. Tax, D.M., Duin, R.P., Support vector data description. Mach. Learn. 54:1 (2004), 45–66.
47. Georgoulas, C., Kotoulas, L., Sirakoulis, G.C., Andreadis, I., Gasteratos, A., Real-time disparity map computation module. Microprocess. Microsyst. 32:3 (2008), 159–170.
48. Montgomery, D.C., Introduction to Statistical Quality Control, 2009, John Wiley & Sons, New York.
49. Blanco, J.-L., Moreno, F.-A., Gonzlez-Jimnez, J., The Mlaga Urban Dataset: High-rate stereo and lidars in a realistic urban scenario. Int. J. Robot. Res. 33:2 (2014), 207–214 http://www.mrpt.org/MalagaUrbanDataset.
50. Scharwächter, T., Enzweiler, M., Franke, U., Roth, S., Efficient multi-cue scene segmentation. German Conference on Pattern Recognition, 2013, Springer, 435–445.
51. Scharwächter, T., Enzweiler, M., Franke, U., Roth, S., Stixmantics: A medium-level model for real-time semantic scene understanding. European Conference on Computer Vision, 2014, Springer, 533–548.