Evaluation of Boosting-SVM and SRM-SVM cascade classifiers in laser and vision-based pedestrian detection

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Volumn , Issue , 2011, Pages 1574-1579

  Ludwig, Oswaldo ^a   Premebida, Cristiano ^a   Nunes, Urbano ^a   Araújo, Rui ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CASCADE CLASSIFIERS; DATA SETS; DESCRIPTORS; DETECTION ERROR; DIRECT IMPACT; ELECTRICAL VEHICLES; EXPERIMENTAL ANALYSIS; FEATURE SPACE; IMAGE-BASED FEATURES; LIGHT CONDITIONS; LINEAR SUPPORT VECTOR MACHINES; LINEAR SVM; LOW SPEED; MACHINE-LEARNING; MONOCULAR CAMERAS; MULTI SENSOR; PEDESTRIAN DETECTION; PEDESTRIAN DETECTION SYSTEM; PROCESSING SYSTEMS; REGIONS OF INTEREST; RESEARCH AND DEVELOPMENT; STRUCTURAL RISK MINIMIZATION; URBAN ENVIRONMENTS; VISION BASED;

COMPUTER VISION; ELECTRIC VEHICLES; INTELLIGENT SYSTEMS; OPTICAL RADAR;

SUPPORT VECTOR MACHINES;

EID: 83755174193     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITSC.2011.6082909     Document Type: Conference Paper

References (16)

1. DARPA. The urban and grand challenges. [online]. http://www.darpa.mil/ grandchallenge/, (accessed: 2010), 2003.
2. ELROB. The european robot trial. [online]. http://www.elrob.org/, (accessed: 2011), 2006.
3. P. Dollar, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: A benchmark. In IEEE Computer Vision and Pattern Recognition (CVPR 09), pages 304-311, Los Alamitos, CA, USA, 2009.
4. M. Enzweiler and D. M. Gavrila. Monocular pedestrian detection: Survey and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2179-2195, Oct. 2009.
5. T. Gandhi and M.M. Trivedi. Pedestrian protection systems: issues, survey, and challenges. Intelligent Transportation Systems, IEEE Transactions on, 8(3):413-430, 2007.
6. David Geronimo, Antonio M. Lopez, Angel D. Sappa, and Thorsten Graf. Survey on pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7):1239-1258, 2010.
7. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In IEEE Computer Vision and Pattern Recognition (CVPR 01), volume 1, pages 511-518, Hawaii, Dec. 2001.
8. Yong Ma and Xiaoqing Ding. Face detection based on hierarchical support vector machines. Pattern Recognition, International Conference on, 1:1051-4651, 2002.
9. C. Premebida, O. Ludwig, and U. Nunes. Lidar and vision-based pedestrian detection system. Journal of Field Robotics, 26(9), 2009.
10. Q. Zhang and R. Pless. Extrinsic calibration of a camera and laser range finder (improves camera calibration). In IEEE Intelligent Robots and Systems Conference (IROS), Sept.-2 Oct. 2004.
11. Dirk Walther and Christof Koch. Modeling attention to salient protoobjects. Neural Networks, 19(9):1395-1407, 2006.
12. Vladimir N. Vapnik. Statistical Learning Theory. John Wiley, 1998.
13. H. Peng, F. Long, and C. Ding. Feature selection based on mutual information criteria of max-dependency, max-relevance, and minredundancy. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(8):1226-1238, Aug. 2005. (Pubitemid 41245053)
14. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In IEEE Computer Vision and Pattern Recognition (CVPR 05), pages 886-893, Washington, DC, USA, 2005.
15. O. Tuzel, F. Porikli, and P. Meer. Region covariance: A fast descriptor for detection and classification. In In Proc. 9th European Conf. on Computer Vision, pages 589-600, 2006.
16. Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, and Vladimir Vapnik. Parallel support vector machines: The cascade SVM. In NIPS, 2004.