Reliable pedestrian recognition combining high-definition LIDAR and vision data

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Volumn , Issue , 2012, Pages 1783-1788

  Kidono, Kiyosumi ^a   Naito, Takashi ^a   Miura, Jun ^b  

^a Rd Management Section   (Japan)

^b TOYOHASHI UNIVERSITY OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

AUTONOMOUS DRIVING; COMPUTATIONAL BURDEN; FALSE POSITIVE; FUSION SYSTEMS; HIGH DEFINITION; KEY TECHNOLOGIES; MULTIPLE THRESHOLD; PEDESTRIAN RECOGNITION; QUANTITATIVE EVALUATION; REGION OF INTEREST; ROAD ENVIRONMENT; SUPPORT VECTOR; VISION DATA; VISION SENSORS;

IMAGE PROCESSING; INTELLIGENT SYSTEMS; SENSORS;

OPTICAL RADAR;

EID: 84871257723     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITSC.2012.6338657     Document Type: Conference Paper

References (21)

1. C. Papageorgiou, T. Poggio, "A trainable system for object detection," Int. Journal of Computer Vision, vol. 38, no. 1, pp. 15-33, 2000.
2. M. Andriluka, S. Roth, and B. Schiele, "People-tracking-by-detection and people-detection-by-tracking," in Proc. Conf. on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
3. P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," in Proc. Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001.
4. N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
5. T. Kobayashi, N. Otsu, "Image Feature Extraction using Gradient Local Auto-Correlations," in Proc. 10th European Conf. on Computer Vision, Part I. LNCS, vol. 5302, pp. 346-358, 2008.
6. T. Watanabe, S. Ito and K. Yokoi, "Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection," in Proc. 3rd Pacific Rim Symp. on Advances in Image and Video Technology, pp. 37-47, 2009.
7. K. O. Arras, O. M. Mozos, W. Burgard, "Using Boosted Features for the Detection of People in 2D Range Data," in Proc. IEEE Int. Conf. on Robotics and Automation, pp. 3402-3407, 2007.
8. L. E. Navarro-Serment, C. Mertz, and M. Hebert, "Pedestrian Detection and Tracking Using Three-Dimensional LADAR Data," in Proc. Int. Conf. on Field and Service Robotics, 2009.
9. L. Spinello, M. Luber and K. Arras, "Tracking People in 3D Using a Bottom-Up Top-Down Detector," in Proc. of 2011 IEEE Int. Conf. on Robotics and Automation, pp. 1304-1310, 2011.
10. G. Gate, A. Breheret and F. Nashashibi, "Centralized Fusion for Fast People Detection in Dense Environment," in Proc. of 2009 IEEE Int. Conf. on Robotics and Automation, pp. 76-81, 2009.
11. C. Premebida, O. Ludwig, and U. Nunes, "LIDAR and Vision-based Pedestrian Detection System," Journal of Field Robotics, vol. 26, pp. 696-711, 2009.
12. L. Huang and M. Barth, "A Novel Multi-Planar LIDAR and Computer Vision Calibration Procedure Using 2D Patterns for Automated Navigation," in Proc. of 2009 IEEE Intelligent Vehicles Symp., pp. 117-122, 2009.
13. S. Thrun, W. Burgard and D. Fox, Probabilistic Robotics, The MIT Press, 2005.
14. G. Toussaint, "Solving geometric problems with the rotating calipers," in Proc. the IEEE MELECON, A10.02/1.4, 1983.
15. K. Kidono, T. Miyasaka, A. Watanabe, T. Naito and J. Miura, "Pedestrian Detection Using High-definition LIDAR," in Proc. of 2011 IEEE Intelligent Vehicles Symp., pp. 405-410, 2011.
16. K. Goto, K. Kidono, Y. Kimura and T. Naito, "Pedestrian Detection and Direction Estimation by Cascade Detector with Multi-classifiers Utilizing Feature Interaction Descriptor," in Proc. of 2011 IEEE Intelligent Vehicles Symp., pp. 224-229, 2011.
17. H. Cao, K. Yamaguchi, M. Ohta, T. Naito, and Y. Ninomiya, "Feature Interaction Descriptor for Pedestrian Detection," IEICE Trans. on Information and Systems, vol. E93.D, pp. 2656-2659, 2010.
18. R. Fan, K. Chang, C. Hsieh, X. Wang and C. Lin, "LIBLINEAR: A Library for Large Linear Classification," Journal of Machine Learning Research, vol. 9, pp. 1871-1874, 2008.
19. J. Platt, "Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods," in Advances in Large Margin Classifiers, MIT Press, pp. 61-74, 1999.
20. S. Blackman, Multiple-Target Tracking with Radar Applications, Artech House, 1986.
21. T. Miyasaka, Y. Ohama and Y. Ninomiya, "Ego-Motion Estimation and Moving Object Tracking using Multi-layer LIDAR," in Proc. of 2009 IEEE Intelligent Vehicles Symp., pp. 151-156, 2009.