Detection and tracking of independently moving objects in urban environments

In this paper we propose an approach for dynamic scene perception from a moving vehicle equipped with a stereo camera rig. The approach is solely based on visual information, hence it is applicable to a large class of autonomous robots working in indoor as well as in outdoor environments. The proposed approach consists of an egomotion estimation based on disparity and optical flow using the Longuet-Higgins-Equations combined with an implicit extended Kalman-Filter. Based on this egomotion estimation a moving object detection and tracking is performed. Each tracked object is labeled with a unique ID while visible in the images. The proposed algorithm was evaluated on numerous challenging real world image sequences.

[1]  Mathias Perrollaz,et al.  Free Space Estimation for Autonomous Navigation , 2007, ICVS 2007.

[2]  Ming-Yu Shih,et al.  Robust Moving Object Detection on Moving Platforms , 2006, PSIVT.

[3]  K. Kanatani,et al.  Estimating the Number of Independent Motions for Multibody Motion Segmentation , 2002 .

[4]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Chingchun Huang,et al.  Motion-based Background Modeling for Moving Object Detection on Moving Platforms , 2007, 2007 16th International Conference on Computer Communications and Networks.

[6]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[7]  W. Marsden.  I and J , 2012 .

[8]  P. Perona,et al.  Dynamic Estimation of Rigid Motion from Perspective Views via Recursive Identification of Exterior Differential Systems with Parameters on a Topological Manifold , 1994 .

[9]  Oliver Schreer.  Stereoanalyse und Bildsynthese , 2007 .

[10]  Tobias Gindele,et al.  Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge , 2008 .

[11]  Julius Ziegler,et al.  Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge , 2008, J. Field Robotics.

[12]  A. ADoefaa,et al.  ? ? ? ? f ? ? ? ? ? , 2003 .

[13]  Larry H. Matthies,et al.  Real-time detection of moving objects from moving vehicles using dense stereo and optical flow , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

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

[15]  Quan Pan,et al.  Real-time and accurate segmentation of moving objects in dynamic scene , 2004, VSSN '04.

[16]  R. Elias,et al.  Clustering points in nD space through hierarchical structures , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[17]  H. C. Longuet-Higgins,et al.  The interpretation of a moving retinal image , 1980, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[18]  Christoph Stiller,et al.  Akquisition, Repräsentation und Nutzung von Wissen in der Fahrerassistenz , 2006 .

[19]  Mikhail Skliar,et al.  Kalman filter for discrete implicit systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[20]  E. R. Davies,et al.  Machine vision - theory, algorithms, practicalities , 2004 .

[21]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[22]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[23]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[24]  Jorge S. Marques,et al.  Performance evaluation of object detection algorithms for video surveillance , 2006, IEEE Transactions on Multimedia.