Registration-based moving object detection from a moving camera

This paper presents 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 stages. Initially, feature points are extracted and tracked through consecutive frames. Then, a RANSAC based approach is used for registering two 3D point sets with known correspondences by means of the quaternion method. Finally, the computed 3D rigid displacement is used to map two consecutive frames into the same coordinate system. Moving objects correspond to those areas with large registration errors. Experimental results, in different scenarios, show the viability of the proposed approach.

[1]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[2]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[3]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Tomaso A. Poggio,et al.  Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Francis Schmitt,et al.  A Solution for the Registration of Multiple 3D Point Sets Using Unit Quaternions , 1998, ECCV.

[6]  Charles E. Thorpe,et al.  Simultaneous localization and mapping with detection and tracking of moving objects , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[7]  Jean-Philippe Tarel,et al.  Real time obstacle detection in stereovision on non flat road geometry through "v-disparity" representation , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[8]  Shigeru Okuma,et al.  Active frame subtraction for pedestrian detection from images of moving camera , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[9]  Jorge Dias,et al.  Vision and Inertial Sensor Cooperation Using Gravity as a Vertical Reference , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Sebastian Thrun,et al.  Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[11]  Michalis E. Zervakis,et al.  A survey of video processing techniques for traffic applications , 2003, Image Vis. Comput..

[12]  Andrew W. Fitzgibbon Robust registration of 2D and 3D point sets , 2003, Image Vis. Comput..

[13]  Michael Brady,et al.  Road feature detection and estimation , 2003, Machine Vision and Applications.

[14]  S. Shankar Sastry,et al.  An Invitation to 3-D Vision , 2004 .

[15]  Agusti Solanas,et al.  3D simultaneous localization and modeling from stereo vision , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[16]  Gaurav S. Sukhatme,et al.  Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments , 2005, Auton. Robots.

[17]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

[18]  Pavel Krsek,et al.  Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm , 2005, Image Vis. Comput..

[19]  Roland Siegwart,et al.  Stereo-Based Ego-Motion Estimation Using Pixel Tracking and Iterative Closest Point , 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

[20]  Sergiu Nedevschi,et al.  Road Surface and Obstacle Detection Based on Elevation Maps from Dense Stereo , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[21]  Angel D. Sappa,et al.  Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection , 2007 .

[22]  Jorge Dias,et al.  Relative Pose Calibration Between Visual and Inertial Sensors , 2007, Int. J. Robotics Res..