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Efficiency Analysis of Object Position and Orientation Detection Algorithms

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Information and Software Technologies (ICIST 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 465))

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Abstract

This work presents a performance evaluation of the state-of-the-art computer vision algorithms for object detection and pose estimation. Depth information from Kinect sensor is used in this work for the estimation task. It is shown, that Kinect depth sensor is more superior for orientation estimation than a regular stereo camera setup. Accuracy and performance of a point cloud alignment ICP method is analyzed and tested. Furthermore, multiple object detectors accuracy and runtime performance is evaluated. Simple but effective techniques are provided for the comparison. Conducted experiments show a maximum object detection accuracy of 90% and speed of 15 fps for standard size VGA images, while ICP alignment performance of 2 fps is achieved. Additional optimizations would be necessary to attain better real-time object detection and pose extraction performance.

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References

  1. Giesbrecht, J.: Development of a Vision-Based Robotic Follower Vehicle. Technical report (2009)

    Google Scholar 

  2. Xiaogang, G., Changhong, W., Zhenshen, Q.: Object Tracking for Autonomous Mobile Robot based on Feedback of Monocular-vision. In: Industrial Electronics and Applications, ICIEA 2007, pp. 467–470 (2007)

    Google Scholar 

  3. Ukida, H., Kawanami, M., Terama, Y.: 3D Object Tracking by Pan-Tilt Moving Cameras and Robot Using Sparse Template Matching and Particle Filter. In: SICE Annual Conference (SICE), pp. 2004–2009 (2011)

    Google Scholar 

  4. Drummond, T., Cipolla, R.: Real-Time Visual Tracking of Complex Structures. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 932–946 (2002)

    Article  Google Scholar 

  5. Lepetit, V., Fua, P.: Monocular Model-based 3D Tracking of Rigid Objects, Found. Trends. Found. Trends. In: Comput. Graph. Vis. 1(1), 1–89 (2005)

    Google Scholar 

  6. Lowe, D.G.: Fitting Parameterized Three-Dimensional Models to Images. In: Transactions on Pattern Analysis and Machine Intelligence (1991)

    Google Scholar 

  7. Rui, L., Zhijiang, D., Fujun, H., Minxiu, K., Lining, S.: Tracking a Moving Object with Mobile Robot Based on Vision. In: I21E International Joint Conference on Neural Networks (2008)

    Google Scholar 

  8. Gönne, C., Rous, M., Kraiss, K.F.: Real-Time Adaptive Colour Segmentation for the RoboCup Middle Size League (2005)

    Google Scholar 

  9. Browning, B., Veloso, M.: Real-Time, Adaptive Color-based Robot Vision (2005)

    Google Scholar 

  10. Lin, C., Setiawan, E.: Object Orientation Recognition Based on SI6 and SVM by Using Stereo Camera. In: International Conference on Robotics and Biomimetics (2009)

    Google Scholar 

  11. Thachasongtham, D., Yoshida, T., de Sorbier, F., Saito, H.: 3D Object Pose Estimation Using Viewpoint Generative Learning. In: Kämäräinen, J.-K., Koskela, M. (eds.) SCIA 2013. LNCS, vol. 7944, pp. 512–521. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  12. Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: Computer Vision and Pattern Recognition, vol.1, pp.I-511–I-518 (2001)

    Google Scholar 

  13. Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  14. Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., Lepetit, V.: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: IEEE International Conference on Computer Vision (ICCV), pp. 858–865 (2011)

    Google Scholar 

  15. Nebehay, G.: Robust Object Tracking Based on Tracking-Learning-Detection, Faculty of Informatics, TU Vienna (2012)

    Google Scholar 

  16. Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: Real-time dense surface mapping and tracking. In: 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136, 26–29 (2011)

    Google Scholar 

  17. Mihalyi, R.G., Pathak, K., Vaskevicius, N., Birk, A.: Uncertainty estimation of AR-marker poses for graph-SLAM optimization in 3D object model generation with RGBD data. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.1807–1813, 3–7 (2013)

    Google Scholar 

  18. Winkler, S.: Model-based pose estimation of 3-D objects from camera images using neural networks. Technical Report IB 515-96-12, Diplomarbeit, Institut für Nachrichten-und Hochfrequenztechnik, Technische Universität Wien, pp. 28–31 (1996)

    Google Scholar 

  19. Huynh, D.Q.: Metrics for 3D Rotations: Comparison and Analysis. J. Math. Imaging 35(2), 155–164 (2009)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Multimedia Engineering Department, Kaunas University of Technology, Studentu St. 50, Kaunas, Lithuania

    Tomas Uktveris

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  1. Tomas Uktveris
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Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Studentu g. 50-409a,, LT-51368, Kaunas, Lithuania

    Giedre Dregvaite

  2. Kaunas University of Technology, Studentu g. 50-409a, LT-51368, Kaunas, Lithuania

    Robertas Damasevicius

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© 2014 Springer International Publishing Switzerland

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Uktveris, T. (2014). Efficiency Analysis of Object Position and Orientation Detection Algorithms. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2014. Communications in Computer and Information Science, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-319-11958-8_24

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  • DOI: https://doi.org/10.1007/978-3-319-11958-8_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11957-1

  • Online ISBN: 978-3-319-11958-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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