A Convenient Multi-camera Self-Calibration Method Based on Human Body Motion Analysis

A novel and convenient multi-camera self-calibration method is proposed in this paper. Different from other calibration methods, our method is done by analyzing human body motion. The only constraint is that several people of different heights are needed to walk around the experimental environment one by one in the calibration period. By this way, two kinds of corresponding points are extracted from synchronous video sequences. One is the centroid of the moving human body. The other is points on the floor, which is extracted by matching floor planes in video sequences. The floor planes registration is based on shadow detection and co-motion feature. Based on these corresponding points, camera parameters and 3D points observed are estimated. The proposed method is tested in our own experimental environment. Experimental results show the accuracy of our calibration method. Our method can satisfy many applications of multi-view computer vision.

[1]  Jean Ponce,et al.  Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  László Havasi,et al.  Estimation of Common Groundplane Based on Co-motion Statistics , 2004, ICIAR.

[3]  Roberto Cipolla,et al.  Silhouette Coherence for Camera Calibration under Circular Motion , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Takeo Kanade,et al.  Shape-From-Silhouette Across Time Part I: Theory and Algorithms , 2005, International Journal of Computer Vision.

[5]  Tomás Pajdla,et al.  Robust Rotation and Translation Estimation in Multiview Reconstruction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Tomás Svoboda,et al.  A Convenient Multicamera Self-Calibration for Virtual Environments , 2005, Presence: Teleoperators & Virtual Environments.

[7]  R. Y. Tsai,et al.  An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision , 1986, CVPR 1986.

[8]  Tomás Pajdla,et al.  Structure from Many Perspective Images with Occlusions , 2002, ECCV.

[9]  David W. Jacobs,et al.  Linear fitting with missing data: applications to structure-from-motion and to characterizing intensity images , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Zhengyou Zhang,et al.  Camera calibration with one-dimensional objects , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[12]  Nicolas Martel-Brisson,et al.  Moving cast shadow detection from a Gaussian mixture shadow model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).