Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking

Sparse representation has been widely studied in visual tracking, which has shown promising tracking performance. Despite a lot of progress, the visual tracking problem is still a challenging task due to appearance variations over time. In this paper, we propose a novel sparse tracking algorithm that well addresses temporal appearance changes, by enforcing template representability and temporal consistency (TRAC). By modeling temporal consistency, our algorithm addresses the issue of drifting away from a tracking target. By exploring the templates' long-term-short-term representability, the proposed method adaptively updates the dictionary using the most descriptive templates, which significantly improves the robustness to target appearance changes. We compare our TRAC algorithm against the state-of-the-art approaches on 12 challenging benchmark image sequences. Both qualitative and quantitative results demonstrate that our algorithm significantly outperforms previous state-of-the-art trackers.

[1]  Horst Bischof,et al.  Real-Time Tracking via On-line Boosting , 2006, BMVC.

[2]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[3]  Zhibin Hong,et al.  Tracking via Robust Multi-task Multi-view Joint Sparse Representation , 2013, 2013 IEEE International Conference on Computer Vision.

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

[5]  Junseok Kwon,et al.  Visual tracking decomposition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Roman P. Pflugfelder,et al.  Clustering of static-adaptive correspondences for deformable object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[9]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Vibhav Vineet,et al.  Struck: Structured Output Tracking with Kernels , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Narendra Ahuja,et al.  Robust visual tracking via multi-task sparse learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Li Bai,et al.  Minimum error bounded efficient ℓ1 tracker with occlusion detection , 2011, CVPR 2011.

[13]  Haibin Ling,et al.  Real time robust L1 tracker using accelerated proximal gradient approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Junzhou Huang,et al.  Robust tracking using local sparse appearance model and K-selection , 2011, CVPR 2011.

[15]  H. Damasio,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision , 1998 .

[16]  Andrea Cavallaro,et al.  Accepted for Publication in Ieee Transactions on Image Processing Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation , 2022 .

[17]  Matthew Stewart,et al.  IEEE Transactions on Cybernetics , 2015, IEEE Transactions on Cybernetics.

[18]  Simone Calderara,et al.  Visual Tracking: An Experimental Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[20]  Huchuan Lu,et al.  Visual tracking via adaptive structural local sparse appearance model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Narendra Ahuja,et al.  Low-Rank Sparse Learning for Robust Visual Tracking , 2012, ECCV.

[22]  Chunhua Shen,et al.  Real-time visual tracking using compressive sensing , 2011, CVPR 2011.

[23]  Junzhou Huang,et al.  Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization , 2010, ECCV.

[24]  Changsheng Xu,et al.  Structural Sparse Tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Lynne E. Parker,et al.  Real-Time Multiple Human Perception With Color-Depth Cameras on a Mobile Robot , 2013, IEEE Transactions on Cybernetics.

[27]  Haibin Ling,et al.  Robust Visual Tracking and Vehicle Classification via Sparse Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Ehud Rivlin,et al.  Robust Fragments-based Tracking using the Integral Histogram , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).