GPU architecture for stationary multisensor pedestrian detection at smart intersections

IEEE Intelligent Vehicles Symposium, Proceedings

Volumn , Issue , 2011, Pages 89-94

  Weimer, Daniel ^a   Köhler, Sebastian ^a   Hellert, Christian ^a   Doll, Konrad ^a   Brunsmann, Ulrich ^a   Krzikalla, Roland ^b  

^a UNIVERSITY OF APPLIED SCIENCES ASCHAFFENBURG   (Germany)

^b SICK AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AROUND THE CLOCK; BACKGROUND SUBTRACTION; BENCHMARK DATABASE; CASCADED CLASSIFIERS; CLASSIFICATION PERFORMANCE; COMPUTE UNIFIED DEVICE ARCHITECTURES; DESCRIPTORS; EXPERIMENTAL STUDIES; EXTERNAL ILLUMINATION; GAUSSIAN KERNEL SUPPORT VECTOR MACHINES; GRAPHICS PROCESSING UNIT; HYPOTHESIS GENERATION; LASER SCANNER; LINEAR KALMAN FILTERS; MIXTURE OF GAUSSIANS; MULTI CORE; MULTI SENSOR; PEDESTRIAN DETECTION; PEDESTRIAN DETECTION AND TRACKING; PEDESTRIAN TRACKING; REAL TIME PERFORMANCE;

COMPUTER GRAPHICS EQUIPMENT; FEATURE EXTRACTION; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LASER APPLICATIONS; PROGRAM PROCESSORS; SCANNING; SUPPORT VECTOR MACHINES;

PEDESTRIAN SAFETY;

EID: 79960789267     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IVS.2011.5940411     Document Type: Conference Paper

References (32)

1. "U.S. Department of Transportation's (DOT's) IntelliDrive program." Internet: http://www.intellidriveusa.org, Jan. 03, 2011 [Jan. 15, 2011].
2. "INTERSAFE-2, cooperative intersection safety." Internet: http://www.intersafe-2.eu, Sep. 02, 2010 [Jan. 15, 2011].
3. "Ko-FAS, Kooperative Sensorik und Kooperative Perzeption für die präventive Sicherheit im Strassenverkehr." Internet: http://www.kofas.de, [Jan. 15, 2011].
4. "World Health Organisation: Global status report on road safety: time for action," 2009.
5. "Statistisches Bundesamt Deutschland: Strassenverkehrsunfälle und Unfallfolgen 2008 nach Ortslagen," 2008, 8(7), page 8.
6. K. Fürstenberg, K. Dietmayer, and V. Willhoeft, "Pedestrian recognition in urban traffic using a vehicle based multilayer laserscanner," in IV, vol. 1, 2002, pp. 31-35.
7. M. Enzweiler and D. M. Gavrila, "Monocular pedestrian detection: Survey and experiments," TPAMI, vol. 31, no. 12, pp. 2179-2195, 2009.
8. S. Wender and K. Dietmayer, "3d vehicle detection using a laser scanner and a video camera," Intell. Transp. Syst., IET, vol. 2, no. 2, pp. 105-112, 2008.
9. S. Schneider, M. Himmelsbach, T. Luettel, and H.-J. Wuensche, "Fusing vision and lidar - synchronization, correction and occlusion reasoning," in IV, 2010, pp. 388-393.
10. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in CVPR, vol. 1, 2005, pp. 886-893.
11. C. Premebida, O. Ludwig, M. Silva, and U. Nunes, "A cascade classifier applied in pedestrian detection using laser and image-based features," in ITSC, 2010, pp. 1153-1159.
12. L. Zhang, B. Wu, and R. Nevatia, "Pedestrian detection in infrared images based on local shape features," in CVPR, 2007, pp. 1-8.
13. C. Stauffer and W. Grimson, "Adaptive background mixture models for real-time tracking," in CVPR, vol. 2, 1999, pp. 2246-2252.
14. S. Bauer, U. Brunsmann, and S. Schlotterbeck-Macht, "FPGA implementation of a HOG-based pedestrian recognition system," in MPC Workshop, 2009, pp. 49-58.
15. Willow Garage, "OpenCV 2.0 and 2.2, open source computer vision library." Internet: http://opencv.willowgarage.com/wiki/ [Jan. 15, 2011].
16. NVIDIA, "Nvidia adds gpu acceleration for opencv application development, sep. 23, 2010," Internet: http://pressroom.nvidia.com/ [Jan. 15, 2011].
17. C. Wojek, G. Dorkó, A. Schulz, and B. Schiele, "Sliding-windows for rapid object class localization: A parallel technique," in DAGM, 2008, pp. 71-81.
18. S. Bauer, S. Köhler, K. Doll, and U. Brunsmann, "Fpga-gpu architecture for kernel svm pedestrian detection," in CVPRW, 2010, pp. 61-68.
19. N. He, J. Cao, and L. Song, "Scale space histogram of oriented gradients for human detection," in ISISE, 2008, pp. 167-170.
20. V. Prisacariu and I. Reid, "fastHOG - a real-time gpu implementation of hog," Department of Engineering Science, Oxford University, Tech. Rep. 2310/09, 2009.
21. M. Piccardi, "Background subtraction technigues: a review," in ICSMC, vol. 4, 2004, pp. 3099-3104.
22. D. Schreiber and M. Rauter, "Gpu-based non-parametric background subtraction for a practical surveillance system," in ICCVW, 2009, pp. 870-877.
23. H. Sugano and R. Miyamoto, "Parallel implementation of pedestrian tracking using multiple cues on GPGPU," in ICCVW, 2009, pp. 900-906.
24. T. Fabián and J. Gaura, "Parallel implementation of recursive background modeling technique in cuda for tracking moving objects in video traffic surveillance," in MEMICS 2008, 2008, pp. 28-36.
25. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, "Multiple kernels for object detection," in ICCV, 2009, pp. 606-613.
26. N. Cristianini and J. Shawe-Taylor, Support Vector Machines and other kernel based learning methods. Cambridge University Press, 2006.
27. A. Carpenter, "cuSVM, a CUDA implementation of support vector classification and regression." Internet: http://www.patternsonascreen.net/ cuSVM.html, [Jan. 15, 2011].
28. Y. Bar-Shalom and T. E. Fortmann, Tracking and data association. Academic Press, 1988.
29. D. Meissner and K. Dietmayer, "Simulation and calibration of infrastructure based laser scanner networks at intersections," in IV, 2010, pp. 670-675.
30. I. Alonso, D. Llorca, M. Sotelo, L. Bergasa, P. R. de Toro, J. Nuevo, M. Ocana, and M. Garrido, "Combination of feature extraction methods for svm pedestrian detection," TITS, vol. 8, no. 2, pp. 292-307, 2007.
31. F. Xu, X. Liu, and K. Fujimura, "Pedestrian detection and tracking with night vision," TITS, vol. 6, no. 1, pp. 63-71, 2005.
32. W. Zhang, G. Zelinsky, and D. Samaras, "Real-time accurate object detection using multiple resolutions," in ICCV, 2007, pp. 1-8.