Dynamic Selection of Characteristics for Feature Based Image Sequence Stabilization

In this document, we deal with the problem of video cameras operating outdoors where the atmospheric conditions and the vibration caused by vehicles passing by make it difficult to sustain the assumption of a fixed camera. Under these circumstances, a method like ours, that dynamically chooses the features that support the transformation from frame to frame, is different to others that fix the features that are tracked. That is because, for vehicular traffic monitoring applications, there are features that appear or disappear as time passes by. This contributes to the flexibility and capability of adaptation of the method to different scenarios. To test the algorithms, we used several image sequences of a camera, experiencing heavy motion due to wind and/or vibration due to vehicular traffic.

[1]  Taghi M. Khoshgoftaar,et al.  An Empirical Study on Estimating Motions in Video Stabilization , 2007, 2007 IEEE International Conference on Information Reuse and Integration.

[2]  Tom E. Bishop,et al.  Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[3]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  P. Atrey,et al.  Timeline-based information assimilation in multimedia surveillance and monitoring systems , 2005, VSSN@MM.

[5]  Joaquín Salas,et al.  Detecting Unusual Activities at Vehicular Intersections , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[6]  Andrea Fusiello,et al.  Image stabilization by features tracking , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[7]  Chong-Wah Ngo,et al.  Selective object stabilization for home video consumers , 2005, IEEE Trans. Consumer Electron..

[8]  Harry Shum,et al.  Full-frame video stabilization with motion inpainting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  David J. Fleet,et al.  Performance of optical flow techniques , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[11]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[12]  K. Ratakonda.  Real-time digital video stabilization for multi-media applications , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[13]  Rama Chellappa,et al.  Evaluation of image stabilization algorithms , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[14]  Sebastiano Battiato,et al.  SIFT Features Tracking for Video Stabilization , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[15]  Wenbin Chen,et al.  Video Stabilization Using Scale-Invariant Features , 2007, 2007 11th International Conference Information Visualization (IV '07).

[16]  Dan Schonfeld,et al.  Online Video Stabilization Based on Particle Filters , 2006, 2006 International Conference on Image Processing.

[17]  Erik G. Learned-Miller,et al.  Learning to Locate Informative Features for Visual Identification , 2008, International Journal of Computer Vision.

[18]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.