Global Consistency Priors for Joint Part-Based Object Tracking and Image Segmentation

Tracking of previously unseen, articulated objects is an active research area. Recently, Deformable Parts Models (DPMs) have been used to improve the online tracking performance for bounding-box trackers. In this paper, we extend the DPM with global priors which enforce consistency with foreground/background segmentation cues. We propose a Dual Decomposition approach and show how to efficiently solve the high-order coupling constraints as a feasible sub-problem. The proposed approach is evaluated on the VOT online tracking benchmark, outperforming the baseline in both tracking accuracy and robustness. We further show that in presence of stable image segmentation cues, the flexibility of a generic DPM generated from a single reference frame can be improved by introducing the concept of part visibility, the visibility-aware DPM (VDPM). This allows for fine-grained articulated object tracking using an automatically generated DPM from a single template image.

[1]  Daphne Koller,et al.  Multi-level inference by relaxed dual decomposition for human pose segmentation , 2011, CVPR 2011.

[2]  Yi Yang,et al.  Articulated pose estimation with flexible mixtures-of-parts , 2011, CVPR 2011.

[3]  Nikos Komodakis,et al.  MRF Optimization via Dual Decomposition: Message-Passing Revisited , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[4]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[6]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Yanning Zhang,et al.  Part-Based Visual Tracking with Online Latent Structural Learning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Patrick Pérez,et al.  Distributed Non-Convex ADMM-inference in Large-scale Random Fields , 2014 .

[11]  Erik B. Sudderth,et al.  Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach , 2015, ICML.

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

[13]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[14]  Andrew Zisserman,et al.  Human Pose Estimation Using a Joint Pixel-wise and Part-wise Formulation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Nikos Komodakis,et al.  MRF Energy Minimization and Beyond via Dual Decomposition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Michael J. Black,et al.  Pose-conditioned joint angle limits for 3D human pose reconstruction , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Lu Zhang,et al.  Preserving Structure in Model-Free Tracking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Patrick Pérez,et al.  Distributed Non-convex ADMM-based inference in large-scale random fields , 2014, BMVC.

[19]  Michael Felsberg,et al.  The Visual Object Tracking VOT2015 Challenge Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

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

[21]  Kun Duan,et al.  Human pose estimation via multi-layer composite models , 2015, Signal Process..