Object Flow: Learning Object Displacement

Modelling the dynamic behaviour of moving objects is one of the basic tasks in computer vision. In this paper, we introduce the Object Flow, for estimating both the displacement and the direction of an object-of-interest. Compared to the detection and tracking techniques, our approach obtains the object displacement directly similar to optical flow, while ignoring other irrelevant movements in the scene. Hence, Object Flow has the ability to continuously focus on a specific object and calculate its motion field. The resulting motion representation is useful for a variety of visual applications (e.g., scene description, object tracking, action recognition) and it cannot be directly obtained using the existing methods.

[1]  Jean-Marc Odobez,et al.  Embedding Motion in Model-Based Stochastic Tracking , 2004, IEEE Transactions on Image Processing.

[2]  Steven M. Seitz,et al.  Filter flow , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  Ian Reid,et al.  fastHOG – a real-time GPU implementation of HOG , 2011 .

[4]  Jitendra Malik,et al.  Motion segmentation and tracking using normalized cuts , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[5]  Tomer Hertz,et al.  Learning distance functions for image retrieval , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[6]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Luc Van Gool,et al.  Coupled Detection and Trajectory Estimation for Multi-Object Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

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

[10]  Ying Wu,et al.  Contextual flow , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Michael J. Black,et al.  Learning Optical Flow , 2008, ECCV.

[12]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[13]  Daniel Cremers,et al.  Anisotropic Huber-L1 Optical Flow , 2009, BMVC.

[14]  C. Bregler,et al.  Large displacement optical flow , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Horst Bischof,et al.  On-line Boosting and Vision , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[16]  Nicu Sebe,et al.  Distance Learning for Similarity Estimation , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[18]  Luc Van Gool,et al.  Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[19]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  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).

[21]  Mubarak Shah,et al.  A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.