Night-time vehicle sensing in far infrared image with deep learning

Journal of Sensors

Volumn 2016, Issue , 2016, Pages

  Wang, Hai ^a   Cai, Yingfeng ^a   Chen, Xiaobo ^a   Chen, Long ^a  

^a JIANGSU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; INFRARED DETECTORS; INFRARED IMAGING; VIDEO STREAMING;

AUTOMOTIVE SENSORS; CONSTANT THRESHOLD; DEEP BELIEF NETWORK (DBN); FALSE DETECTIONS; FALSE POSITIVE RATES; INFRA-RED SENSOR; NIGHT VISION SYSTEMS; VEHICLE DETECTION;

VEHICLES;

EID: 84953270625     PISSN: 1687725X     EISSN: 16877268     Source Type: Journal    
DOI: 10.1155/2016/3403451     Document Type: Article

References (23)

1. http://asirt.org/Initiatives/Informing-Road-Users/Road-Safety-Facts/Road-Crash-Statistics.
2. S. Sivaraman and M. M. Trivedi, "Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis," IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1773-1795, 2013.
3. H. Wang, C. Yuan, and Y. Cai, "Smart road vehicle sensing system based on monocular vision," Optik, vol. 126, no. 4, pp. 386-390, 2015.
4. A. B. Hillel, R. Lerner, D. Levi, and G. Raz, "Recent progress in road and lane detection: a survey," Machine Vision and Applications, vol. 25, no. 3, pp. 727-745, 2014.
5. R. Benenson, M. Omran, J. Hosang, and B. Schiele, "Ten years of pedestrian detection, what have we learned? in Computer Vision-ECCV 2014 Workshops, vol. 8926 of Lecture Notes in Computer Science, pp. 613-627, Springer, 2015.
6. F. Zaklouta and B. Stanciulescu, "Real-time traffic sign recognition in three stages," Robotics and Autonomous Systems, vol. 62, no. 1, pp. 16-24, 2014.
7. S. Sivaraman and M. M. Trivedi, "A general active-learning framework for on-road vehicle recognition and tracking," IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp. 267-276, 2010.
8. C. Hermes, J. Einhaus, M. Hahn, C. Wohler, and F. Kummert, "Vehicle tracking andmotion prediction in complex urban scenarios," in Proceedings of the IEEE IntelligentVehicles Symposium (IV 10), pp. 26-33, IEEE, June 2010.
9. A. Ramirez, E. Ohn-Bar, and M. Trivedi, "Integrating motion and appearance for overtaking vehicle detection," in Proceedings of the 25th IEEE Intelligent Vehicles Symposium, pp. 96-101, IEEE, June 2014.
10. J. Arrospide, L. Salgado, M. Nieto, and F. Jaureguizar, "Onboard robust vehicle detection and tracking using adaptive quality evaluation," in Proceedings of the 15th IEEE International Conference on Image Processing (ICIP 08), pp. 2008-2011, IEEE, San Diego, Calif, USA, October 2008.
11. L. Andreone, P. Antonello, M. Bertozzi, A. Broggi, A. Fascioli, and D. Ranzato, "Vehicle detection and localization in infra-red images," in Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, pp. 141-146, Singapore, 2002.
12. B. Besbes, A. Apatean, A. Rogozan, and A. Bensrhair, "Combining SURF-based local and global features for road obstacle recognition in far infrared images," in Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems (ITSC 10), pp. 1869-1874, September 2010.
13. P. Hurney, P. Waldron, F. Morgan, E. Jones, and M. Glavin, "Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors," IET Intelligent Transport Systems, vol. 9, no. 1, pp. 75-85, 2015.
14. Q. Liu, J. Zhuang, and S. Kong, "Detection of pedestrians at night time using learning-based method and head validation," in Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST 12), pp. 398-402, IEEE, July 2012.
15. O. Pauplin and J. Jiang, "DBN-based structural learning and optimisation for automated handwritten character recognition," Pattern Recognition Letters, vol. 33, no. 6, pp. 685-692, 2012.
16. Z. Sun, G. Bebis, and R. Miller, "On-road vehicle detection: a review," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 694-711, 2006.
17. S. Sivaraman and M. M. Trivedi, "Active learning for on-road vehicle detection: a comparative study," Machine Vision and Applications, vol. 25, no. 3, pp. 599-611, 2014.
18. S. S. Teoh and T. Braunl, "Symmetry-based monocular vehicle detection system," Machine Vision and Applications, vol. 23, no. 5, pp. 831-842, 2012.
19. R. Sindoori, K. S. Ravichandran, and B. Santhi, "Adaboost technique for vehicle detection in aerial surveillance," International Journal of Engineering & Technology, vol. 5, no. 2, pp. 765-769, 2013.
20. J. Cui, F. Liu, Z. Li, and Z. Jia, "Vehicle localisation using a single camera," in Proceedings of the IEEE Intelligent Vehicles Symposium (IV 10), pp. 871-876, June 2010.
21. G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, no. 7, pp. 1527-1554, 2006.
22. S. Zhong, Y. Liu, and Y. Liu, "Bilinear deep learning for image classification," in Proceedings of the 19th ACM International Conference on Multimedia, pp. 343-352, ACM, 2011.
23. F. Wood and G. E. Hinton, "Training products of experts by minimizing contrastive divergence," Tech. Rep. , Brown University, Providence, RI, USA, 2012.