Objectives

Project Objectives

Objective 1

High-performance embedded computing platform
UTC will contribute with their experience in hardware and vision based processing to the specification of the heterogeneous multi-core computing platform.

Objective 2

Environment sensors
UTC will contribute in the area of stereo-vision sensors including parametric and reconfigurable basic algorithms (calibration, cross-calibration, un-distort, stereo-reconstruction, low level temporal and / or multi-sensor fusion, automatic real-time sensors adaptation to environment / application requirements, auto tests for validating the sensors / sensory system well-functioning, various data acquisition options) and embedded hardware integration.


Objective 3

Perception and localization
UTC will contribute to modeling perception components (Task 25.1), reconfigurable perception components (Task 25.2) and reconfigurable environment modeling (Task 25.3) oriented towards the stereo-vision sensor.


Objective 4

Reasoning and adaptation
UTC will contribute to reasoning and planning, focusing on situation and risk assessment based on the updated environment representation.


Objective 5

Professional service robots
UTC integrates the components developed in WP22 and WP25 related to stereo-vision based perception into the demonstrator platform.


Objective 6

Dissemination, exploitation, standardization, and certification

References


1. J. Huang, V. Blanz, B. Heisele, "Face Recognition with Support Vector Machines and 3D Head Models" in Center for Biological and Computer Learning M.I.T Cambridge MA USA and Computer Graphics Research Group, Freiburg, Germany:University of Freiburg.
2. N. Einecke, J. Eggert, "Block-matching stereo with relaxed fronto-parallel assumption", 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 700-705, 2014.
3. Lyndon N. Smith, Melvyn L. Smith, "Stereo vision technology for object measurement", Proc. SPIE 5011 Machine Vision Applications in Industrial Inspection XI, vol. 307, May 19, 2003.
4. D. Scharstein, R. Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms", International Journal of Computer Vision, vol. 47, no. 1-3, pp. 7-42, April-June 2002.
5. I. Ernst, H. Hirschmüller, G. Bebis, R. Boyle, B. Parvin, D. Koracin, P. Remagnino, F. Porikli et al., "Mutual Information Based Semi-Global Stereo Matching on the GPU" in Advances in Visual Computing, Berlin Heidelberg:Springer, vol. 5358, pp. 228-239, 2008.
6. J. I. Woodfill, G. Gordon, D. Jurasek, T. Brown, R. Buck, "The Tyzx DeepSea G2 Vision System A Taskable Embedded Stereo Camera", Computer Vision and Pattern Recognition Workshop 2006. CVPRW '06. Conference on, pp. 126-126, 2006.
7. M. P. Muresan, M. Negru, S. Nedevschi, "Improving local stereo algorithms using binary shifted windows fusion and smoothness constraint", Intelligent Computer Communication and Processing (ICCP) 2015 IEEE International Conference on, pp. 179-185, 2015.
8. B. Ranft, T. Strauß, "Modeling arbitrarily oriented slanted planes for efficient stereo vision based on block matching", 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1941-1947, 2014.
9. N. Einecke, J. Eggert, "Stereo image warping for improved depth estimation of road surfaces", Intelligent Vehicles Symposium (IV) 2013 IEEE, pp. 189-194, 2013.
10. N. Einecke, J. Eggert, "Block-matching stereo with relaxed fronto-parallel assumption", 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 700-705, 2014.
11. N. Einecke, J. Eggert, "A multi-block-matching approach for stereo", 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 585-592, 2015.
12. I. Haller, S. Nedevschi, "Design of Interpolation Functions for Subpixel-Accuracy Stereo-Vision Systems", Image Processing IEEE Transactions on, vol. 21, pp. 889-898, 2012.
13. Zbontar Jure, Yann LeCun, "Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches", arXiv preprint arXiv: 1510, vol. 05970, 2015.
14. A. Geiger, P. Lenz, C. Stiller, R. Urtasun, "Vision meets robotics: The KITTI dataset", Int. J. Rob. Res., vol. 32, pp. 1231-1237, 2013.
15. D. Scharstein, R. Szeliski, "High-accuracy stereo depth maps using structured light", Computer Vision and Pattern Recognition 2003. Proceedings. 2003 IEEE Computer Society Conference on, pp. I-195-I-202, 2003.
16. J. Zbontar and Y. LeCun, “Computing the stereo matching cost with a convolutional neural network,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, pp. 1592–1599
17. W. Luo, A. G. Schwing, and R. Urtasun, “Efficient Deep Learning for Stereo Matching,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016, pp. 5695–5703.
18. H. Hirschmuller, “Stereo Processing by Semiglobal Matching and Mutual Information,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 30, no. 2, pp. 328–341, Feb 2008.
19. R. Spangenberg, T. Langner, S. Adfeldt, and R. Rojas, “Large scale Semi-Global Matching on the CPU,” in Intelligent Vehicles Symposium Proceedings, 2014 IEEE, June 2014, pp. 195–201.
20. R. Zabih and J. Woodfill, “Non-parametric local transforms for computing visual correspondence,” in Computer Vision ECCV ’94, ser. Lecture Notes in Computer Science, J.-O. Eklundh, Ed. Springer Berlin Heidelberg, 1994, vol. 801, pp. 151–158.
21. A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite,” in Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
22. M. Humenberger, T. Engelke, and W. Kubinger, “A census-based stereo vision algorithm using modified Semi-Global Matching and plane fitting to improve matching quality,” in Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, June 2010, pp. 77–84.
23. K. Yamaguchi, D. McAllester, and R. Urtasun, “Efficient joint segmentation, occlusion labeling, stereo and flow estimation,” in European Conference on Computer Vision. Springer, 2014, pp. 756–771.
24. F. Gney and A. Geiger, “Displets: Resolving stereo ambiguities using object knowledge,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, pp. 4165–4175.
25. L. Schneider, M. Cordts, T. Rehfeld, D. Pfeiffer, M. Enzweiler, U. Franke, M. Pollefeys, and S. Roth, “Semantic Stixels: Depth is not enough,” in 2016 IEEE Intelligent Vehicles Symposium (IV), June 2016, pp. 110–117.
26. S. Birchfield and C. Tomasi, “Depth discontinuities by pixel-to-pixel stereo,” in Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), Jan 1998, pp. 1073–1080.
27. R. Spangenberg, T. Langner, and R. Rojas, “Weighted Semi-Global Matching and Center-Symmetric Census Transform for Robust Driver Assistance.”
28. W. S. Fife and J. K. Archibald, “Improved Census Transforms for Resource-Optimized Stereo Vision,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 1, pp. 60–73, Jan 2013.
29. M. Loghman and J. Kim, “SGM-based dense disparity estimation using adaptive Census transform,” in 2013 International Conference on Connected Vehicles and Expo (ICCVE), Dec 2013, pp. 592–597.
30. V. C. Miclea and S. Nedevschi, “Optimizing Census-based Semi Global Matching by genetic algorithms,” in 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP), Sept 2016, pp. 193–198.
31. P. Pinggera, D. Pfeiffer, U. Franke, and R. Mester, “Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice,” in Computer Vision ECCV 2014, ser. Lecture Notes in Computer Science, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Springer International Publishing, 2014, vol. 8690, pp. 96–111.
32. Y. Cheng and L. Matthies, “Stereovision Bias Removal by Autocorrelation,” in Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, Jan 2015, pp. 1153–1160.
33. I. Haller, C. Pantilie, T. Marita, and S. Nedevschi, “Statistical method for sub-pixel interpolation function estimation,” in Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on, Sept 2010, pp. 1098–1103.
34. V.-C. Miclea, C.-C. Vancea, and S. Nedevschi, “New sub-pixel interpolation functions for accurate real-time stereo-matching algorithms,” in Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on, Sept 2015, pp. 173–178.
35. C. C. Vancea, V. C. Miclea, and S. Nedevschi, “Improving stereo reconstruction by sub-pixel correction using histogram matching,” in 2016 IEEE Intelligent Vehicles Symposium (IV), June 2016, pp. 335–341.
36. A. D. Costea and S. Nedevschi, “Fast traffic scene segmentation using multi-range features from multi-resolution filtered and spatial context channels,” in 2016 IEEE Intelligent Vehicles Symposium (IV), June 2016, pp. 328–334.
37. C. Banz, P. Pirsch, and H. Blume, “Evaluation of Penalty Functions for Semi-Global Matching Cost Aggregation,” ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 1–6, Jul. 2012.
38. C. Pantilie and S. Nedevschi, “SORT-SGM: Subpixel Optimized Real-Time Semiglobal Matching for Intelligent Vehicles,” Vehicular Technology, IEEE Transactions on, vol. 61, no. 3, pp. 1032–1042, March 2012.
39. D. Scharstein, H. Hirschmuller, Y. Kitajima, G. Krathwohl, N. Nesic, X. Wang, and P. Westling, “High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth,” in Pattern Recognition - 36th German Conference, GCPR 2014, Munster, September 2-5, pp. 31–42.