Abstract
This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios.
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References
Amir, S., Barhoumi, W., Zagrouba, E.: A robust framework for joint background/foreground segmentation of complex video scenes filmed with freely moving camera. Pattern Analysis and Applications 46(2), 175–205
Benjemaa, R., Schmitt, F.: A solution for the registration of multiple 3D point sets using unit quaternions. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 34–50. Springer, Heidelberg (1998)
Besl, P., McKay, N.: A method for registration of 3D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1988)
Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vision Comput. 10(3), 145–155 (1992)
Chetverikov, D., Stepanov, D., Krsek, P.: Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image and Vision Computing 23(1), 299–309 (2005)
Chui, H., Rangarajan, A.: A feature registration framework using mixture models. In: MMBIA 2000: Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 190–197 (2000)
Dahyot, R.: Unsupervised camera motion estimation and moving object detection in videos. In: Proc. of the Irish Machine Vision and Image Processing, Dublin, Ireland (August 2006)
Fitzgibbon, A.: Robust registration of 2D and 3D point sets. Image and Vision Computing 21(13), 1145–1153 (2003)
Gerónimo, D., Sappa, A.D., López, A., Ponsa, D.: Adaptive image sampling and windows classification for on–board pedestrian detection. In: Proc. Int. Conf. on Computer Vision Systems, Bielefeld, Germany (2007)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. of The Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151 (1988)
Hashiyama, T., Mochizuki, D., Yano, Y., Okuma, S.: Active frame subtraction for pedestrian detection from images of moving camera. In: Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, Washington, USA, pp. 480–485 (October 2003)
Horn, B.: Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America A 4, 629–642 (1987)
Jian, B., Vemuri, B.: A robust algorithm for point set registration using mixture of Gaussians. In: 10th IEEE International Conference on Computer Vision, Beijing, China, October 17-20, pp. 1246–1251 (2005)
Kastrinaki, V., Zervakis, M., Kalaitzakis, K.: A survey of video processing techniques for traffic applications. Image and Vision Computing 21(4), 359–381 (2003)
Kong, H., Audibert, J., Ponce, J.: Detecting abandoned objects with a moving camera. IEEE Transactions on Image Processing 19(8), 2201–2210 (2010)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2(60), 91–110 (2004)
Ma, Y., Soatto, S., Kosecká, J., Sastry, S.: An Invitation to 3D Vision: From Images to Geometric Models. Springer, New York (2004)
Milella, A., Siegwart, R.: Stereo-based ego-motion estimation using pixel tracking and iterative closest point. In: Proc. IEEE Int. Conf. on Mechatronics and Automation, USA (January 2006)
Oniga, F., Nedevschi, S., Meinecke, M., To, T.: Road surface and obstacle detection based on elevation maps from dense stereo. In: Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Seattle, USA, pp. 859–865 (September 2007)
Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. In: Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Puerto Rico (June 1997)
Radke, R., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: A systematic survey. IEEE Trans. on Image Processing 14(3), 294–307 (2003)
Restrepo-Specht, A., Sappa, A.D., Devy, M.: Edge registration versus triangular mesh registration, a comparative study. Signal Processing: Image Communication 20(9-10), 853–868 (2005)
Salvi, J., Matabosch, C., Fofi, D., Forest, J.: A review of recent range image registration methods with accuracy evaluation. Image Vision Computing 25(5), 578–596 (2007)
Sappa, A.D., Dornaika, F., Gerónimo, D., López, A.: Registration-based moving object detection from a moving camera. In: Proc. on Workshop on Perception, Planning and Navigation for Intelligent Vehicles, Nice, France (September 2008)
Sharp, G., Lee, S., Wehe, D.: ICP registration using invariant features. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 90–102 (2002)
Shimizu, S., Yamamoto, K., Wang, C., Satoh, Y., Tanahashi, H., Niwa, Y.: Moving object detection by mobile stereo omni-directional system (SOS) using spherical depth image. Pattern Analysis and Applications (2), 113–126
Taleghani, S., Aslani, S., Saeed, S.: Robust moving object detection from a moving video camera using neural network and kalman filter. In: Iocchi, L., Matsubara, H., Weitzenfeld, A., Zhou, C. (eds.) RoboCup 2008. LNCS, vol. 5399, pp. 638–648. Springer, Heidelberg (2009)
Tarel, J.-P., Civi, H., Cooper, D.B.: Pose estimation of free-form 3D objects without point matching using algebraic surface models. In: Proceedings of IEEE Worshop Model Based 3D Image Analysis, Mumbai, India, pp. 13–21 (1998)
Vedaldi, A.: An open implementation of the SIFT detector and descriptor. Technical Report 070012, UCLA CSD (2007)
Wang, C., Thorpe, C., Thrun, S.: Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas. In: Proc. IEEE Int. Conf. on Robotics and Automation, Taipei, Taiwan, pp. 842–849 (September 2003)
Wang, H., Zhang, Q., Luo, B., Wei, S.: Robust mixture modelling using multivariate t-distribution with missing information. Pattern Recogn. Lett. 25(6), 701–710 (2004)
Wange, L., Yung, N.: Extraction of moving objects from their background based on multiple adaptive thresholds and boundary evaluation. IEEE Transactions on Intelligent Transportation Systems 11(1), 40–51 (2010)
Wolf, D., Sukhatme, G.: Mobile robot simultaneous localization and mapping in dynamic environments. Autonomous Robots 19(1), 53–65 (2005)
Yu, Q., Medioni, G.: Map-enhanced detection and tracking from a moving platform with local and global data association. In: Proc. IEEE Workshops on Motion and Video Computing, Austin, Texas (February 2007)
Yu, Q., Medioni, G.: A GPU-based implementation of motion detection from a moving platform. In: Proc. IEEE Workshops on Computer Vision and Pattern Recognition, Anchorage, Alaska (June 2008)
Zheng, B., Ishikawa, R., Oishi, T., Takamatsu, J., Ikeuchi, K.: A fast registration method using IP and its application to ultrasound image registration. IPSJ Transactions on Computer Vision and Applications 1, 209–219 (2009)
Zheng, B., Takamatsu, J., Ikeuchi, K.: An adaptive and stable method for fitting implicit polynomial curves and surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(3), 561–568 (2010)
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Sappa, A.D., Gerónimo, D., Dornaika, F., Rouhani, M., López, A.M. (2012). Moving Object Detection from Mobile Platforms Using Stereo Data Registration. In: Ogiela, M., Jain, L. (eds) Computational Intelligence Paradigms in Advanced Pattern Classification. Studies in Computational Intelligence, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24049-2_3
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DOI: https://doi.org/10.1007/978-3-642-24049-2_3
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