Tracking vulnerable road users with severe occlusion by adaptive part filter modeling

The visual based tracking has been an active area among the research community in recent years. Its application can be broadly found in intelligent transportation systems like advanced driver assistance systems (ADAS) or autonomous vehicles. One objective is to monitor the trajectory and behavior of the vulnerable road users (VRUs), e.g., pedestrians and cyclists, to prevent the probable collisions. However, the tracking of VRUs is still challenging, especially in cases, where severe occlusion occurs and the tracker fails due to the abrupt change of object appearance. To tackle this problem we propose a new tracking approach leveraging the part based trackers, which are built based on correlation filters. In this method both the number and the size of part filters are adapted to the current appearance of the object, to eliminate the influence by occluded parts. Experimental results show that our tracker performs robust with respect to occlusions, especially in cases, where long term and severe occlusions appear. Due to a sophisticated design, a real time processing performance can also be achieved by our approach.

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

[2]  Jing Peng,et al.  SVM vs regularized least squares classification , 2004, ICPR 2004.

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

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

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

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

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

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

[9]  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).

[10]  Shai Avidan,et al.  Support Vector Tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[12]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[13]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  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).

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

[16]  T. Poggio,et al.  Chapter 7 Regularized Least-Squares Classification , 2003 .

[17]  Xi Zhang,et al.  Tracking benchmark and evaluation for manipulation tasks , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

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

[19]  Wei Xiang,et al.  Part-Based Tracking with Appearance Learning and Structural Constrains , 2014, ICONIP.

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

[21]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Horst Bischof,et al.  PROST: Parallel robust online simple tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  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.

[24]  Jianke Zhu,et al.  A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration , 2014, ECCV Workshops.

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

[26]  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).

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

[28]  Junseok Kwon,et al.  Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive Basin Hopping Monte Carlo sampling , 2009, CVPR.

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

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

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

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

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