A unified 2D-3D video scene change detection framework for mobile camera platforms

In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approach integrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms the scene changes by finding inconsistent appearances in a set of aligned images using the classical MRF background subtraction technique. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from a camera placed on a moving vehicle and the experiments show that our proposed method outperforms previous works in scene change detection, which suggested the feasibility of our approach.

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

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

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

[4]  Svetha Venkatesh,et al.  Passenger monitoring in moving bus video , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[5]  Berthold K. P. Horn Robot vision , 1986, MIT electrical engineering and computer science series.

[6]  Yihong Gong,et al.  Modeling Using Time Dependent Markov Random Field With Image Pyramid , 2004 .

[7]  Vipin Kumar,et al.  Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data , 2003, SDM.

[8]  P. Anandan,et al.  A unified approach to moving object detection in 2D and 3D scenes , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[9]  Tomas Akenine-Möller,et al.  Optimized View Frustum Culling Algorithms for Bounding Boxes , 2000, J. Graphics, GPU, & Game Tools.

[10]  Manolis I. A. Lourakis,et al.  Independent 3D motion detection using residual parallax normal flow fields , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  Montse Pardàs,et al.  A Unified Framework for Consistent 2-D/3-D Foreground Object Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[13]  Svetha Venkatesh,et al.  Pedestrian detection for mobile bus surveillance , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Different Scenes , 2008, ECCV.

[16]  Rama Chellappa,et al.  Moving targets detection using sequential importance sampling , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[17]  T. Ebrahimi,et al.  Change detection and background extraction by linear algebra , 2001, Proc. IEEE.

[18]  Sei-Wang Chen,et al.  Automatic change detection of driving environments in a vision-based driver assistance system , 2003, IEEE Trans. Neural Networks.

[19]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[20]  Hichem Sahli,et al.  MRF-Based Foreground Detection in Image Sequences from a Moving Camera , 2006, 2006 International Conference on Image Processing.

[21]  Long Quan,et al.  Linear N-Point Camera Pose Determination , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[23]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .