Tracking Objects with Severe Occlusion by Adaptive Part Filter Modeling - In Traffic Scenes and Beyond

Vision-based object tracking approach has been drawing increased attention from both the academia and the industry in recent years. One of its most successful applications is the monitoring system, which can be installed on infrastructures or on mobile platforms (e.g., vehicles) to track pedestrians or bicyclists in a pre-defined region and further to prevent probable accidents. Despite tremendous progress achieved, the task of visual tracking is still challenging, especially in dealing with severe occlusions, where the tracker may fail due to the abrupt change of object appearance. Such case is common not only in traffic scenarios but also in other tracking tasks. Aiming to tackle this problem, in this paper, we propose a new tracking approach by adopting part based trackers, which are built in the form of correlation filter. In this approach, the occluded object parts are identified by leveraging the knowledge derived from image features and filter responses. With the help of a masking process, visible object areas are acquired in a pixel-wise precision and utilized to build part filters. As both the number and size of part filters are adapted to the current object appearance, the influence of occlusions can be significantly suppressed. Experimental results on traffic sequences demonstrate that our tracker performs robust against occlusion, especially in cases, where long term and severe occlusions appear. A further experiment on the standard benchmark proves that our approach outperforms state-of-the-art methods in tracking various object classes under varied circumstances. Furthermore, the proposed tracker is sophisticatedly designed and is feasible for real time applications.

[1]  B. Slack,et al.  The Geography of Transport Systems , 2006 .

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

[3]  Dit-Yan Yeung,et al.  Understanding and Diagnosing Visual Tracking Systems , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[4]  Kyoung Mu Lee,et al.  Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive Basin Hopping Monte Carlo sampling , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Stan Sclaroff,et al.  MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization , 2014, ECCV.

[6]  Michael Felsberg,et al.  Adaptive Color Attributes for Real-Time Visual Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Simon Lucey,et al.  Learning Background-Aware Correlation Filters for Visual Tracking , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Changsheng Xu,et al.  Partial Occlusion Handling for Visual Tracking via Robust Part Matching , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[10]  Horst Bischof,et al.  On-line Random Forests , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

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

[12]  Martin Lauer,et al.  Detection and Orientation Estimation for Cyclists by Max Pooled Features , 2017, VISIGRAPP.

[13]  Simon Lucey,et al.  Multi-channel Correlation Filters , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Ales Leonardis,et al.  Visual Object Tracking Performance Measures Revisited , 2015, IEEE Transactions on Image Processing.

[15]  Rui Caseiro,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence High-speed Tracking with Kernelized Correlation Filters , 2022 .

[16]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Michael Felsberg,et al.  Learning Spatially Regularized Correlation Filters for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[18]  Martin Lauer,et al.  Vehicle Tracking at Nighttime by Kernelized Experts With Channel-Wise and Temporal Reliability Estimation , 2018, IEEE Transactions on Intelligent Transportation Systems.

[19]  Zhe Chen,et al.  MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[21]  Stanley T. Birchfield,et al.  Adaptive fragments-based tracking of non-rigid objects using level sets , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Bohyung Han,et al.  Learning Multi-domain Convolutional Neural Networks for Visual Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Gang Wang,et al.  Real-time part-based visual tracking via adaptive correlation filters , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[26]  Jon Shaw The Geography of Transport Systems, 4th edition, J.-P. Rodrigue, C. Comtois, B. Slack (Eds.), Routledge, Abingdon (2017), (440pp. £140 (hardback) £46.99 (paperback) £23.50 (ebook) ISBN 978-1138669574) , 2019 .

[27]  Rui Caseiro,et al.  Beyond Hard Negative Mining: Efficient Detector Learning via Block-Circulant Decomposition , 2013, 2013 IEEE International Conference on Computer Vision.

[28]  Martin Lauer,et al.  Tracking vulnerable road users with severe occlusion by adaptive part filter modeling , 2017, 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES).

[29]  Ales Leonardis,et al.  Robust Visual Tracking Using an Adaptive Coupled-Layer Visual Model , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Gang Wang,et al.  Visual Tracking via Temporally Smooth Sparse Coding , 2015, IEEE Signal Processing Letters.

[31]  Matej Kristan,et al.  A Graphical Model for Rapid Obstacle Image-Map Estimation from Unmanned Surface Vehicles , 2014, ACCV.

[32]  Qingming Huang,et al.  Hedged Deep Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Ming-Hsuan Yang,et al.  Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[34]  Simon Lucey,et al.  Correlation filters with limited boundaries , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[36]  Martin Lauer,et al.  Fast Cyclist Detection by Cascaded Detector and Geometric Constraint , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[37]  Emilio Maggio,et al.  Multi-part target representation for color tracking , 2005, IEEE International Conference on Image Processing 2005.