Non-Causal Tracking by Deblatting

Tracking by Deblatting stands for solving an inverse problem of deblurring and image matting for tracking motion-blurred objects. We propose non-causal Tracking by Deblatting which estimates continuous, complete and accurate object trajectories. Energy minimization by dynamic programming is used to detect abrupt changes of motion, called bounces. High-order polynomials are fitted to segments, which are parts of the trajectory separated by bounces. The output is a continuous trajectory function which assigns location for every real-valued time stamp from zero to the number of frames. Additionally, we show that from the trajectory function precise physical calculations are possible, such as radius, gravity or sub-frame object velocity. Velocity estimation is compared to the high-speed camera measurements and radars. Results show high performance of the proposed method in terms of Trajectory-IoU, recall and velocity estimation.

[1]  Bernard Ghanem,et al.  A Benchmark and Simulator for UAV Tracking , 2016, ECCV.

[2]  Jiri Matas,et al.  Robust scale-adaptive mean-shift for tracking , 2013, Pattern Recognition Letters.

[3]  Arnold W. M. Smeulders,et al.  Tracking for Half an Hour , 2017, ArXiv.

[4]  Jiri Matas,et al.  Discriminative Correlation Filter with Channel and Spatial Reliability , 2017, CVPR.

[5]  Jiri Matas,et al.  FuCoLoT - A Fully-Correlational Long-Term Tracker , 2018, ACCV.

[6]  Luc Van Gool,et al.  Multi-view Tracking of Multiple Targets with Dynamic Cameras , 2014, GCPR.

[7]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Michael Felsberg,et al.  The Sixth Visual Object Tracking VOT2018 Challenge Results , 2018, ECCV Workshops.

[9]  Vineet Gandhi,et al.  Long-Term Visual Object Tracking Benchmark , 2017, ACCV.

[10]  Jiri Matas,et al.  Robust scale-adaptive mean-shift for tracking , 2013, Pattern Recognit. Lett..

[11]  Hrabalík Aleš Implementing and Applying Fast Moving Object Detection on Mobile Devices , 2017 .

[12]  Ming Tang,et al.  High-Speed Tracking with Multi-kernel Correlation Filters , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Feng Li,et al.  Blurred target tracking by Blur-driven Tracker , 2011, 2011 International Conference on Computer Vision.

[14]  Ling Shao,et al.  Visual Tracking Under Motion Blur , 2016, IEEE Transactions on Image Processing.

[15]  Jiri Matas,et al.  The World of Fast Moving Objects , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Jiri Matas,et al.  Intra-Frame Object Tracking by Deblatting , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[17]  Jan Kotera,et al.  Motion Estimation and Deblurring of Fast Moving Objects , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[18]  Anna Hilsmann,et al.  Model-based motion blur estimation for the improvement of motion tracking , 2017, Comput. Vis. Image Underst..

[19]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[20]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Carlo Tomasi,et al.  Features for Multi-target Multi-camera Tracking and Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Zhenyu He,et al.  The Visual Object Tracking VOT2016 Challenge Results , 2016, ECCV Workshops.