Structure and appearance preserving network flow for multi-object tracking

Tracking-by-detection with temporal smoothness has recently attracted increasing attentions in the field of multi-object tracking. Occlusions and clutter are two key problems. To address these problems, this paper proposes a new structure and appearance preserving network flow (SAPNF) with tracking-by-detection, introducing spatial structural configuration and appearance overlapping constraint from frame to frame. One crucial aspect in SAPNF is to consider structure information and appearance smoothness simultaneously which benefits from each other. Unlike previous studies that only learn spatial information or appearance smoothness, a unified min-cost flow with the proposed new structure and appearance induces to track multi-object in crowded and cluttered scenes. Experiments on PETS and TUD benchmarks show that SAPNF performs the comparative results in comparison with alternative methods.

[1]  Brendan J. Frey,et al.  Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.

[2]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[3]  Ivan Laptev,et al.  On pairwise costs for network flow multi-object tracking , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Bernt Schiele,et al.  Monocular 3D pose estimation and tracking by detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Charless C. Fowlkes,et al.  Globally-optimal greedy algorithms for tracking a variable number of objects , 2011, CVPR 2011.

[6]  Lu Zhang,et al.  Structure Preserving Object Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Ramakant Nevatia,et al.  Global data association for multi-object tracking using network flows , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Honggang Zhang,et al.  Joint Patch and Multi-label Learning for Facial Action Unit and Holistic Expression Recognition , 2016, IEEE Transactions on Image Processing.

[9]  Konrad Schindler,et al.  Detection- and Trajectory-Level Exclusion in Multiple Object Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Konrad Schindler,et al.  Multi-target tracking by continuous energy minimization , 2011, CVPR 2011.

[11]  X. Jin.  Factor graphs and the Sum-Product Algorithm , 2002 .

[12]  Ian D. Reid,et al.  Joint tracking and segmentation of multiple targets , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Chen Wang,et al.  Semantic object segmentation via detection in weakly labeled video , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[15]  Wenhan Luo,et al.  Multiple object tracking: A literature review , 2014, Artif. Intell..

[16]  Pascal Fua,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiple Object Tracking Using K-shortest Paths Optimization , 2022 .

[17]  Andreas Geiger,et al.  FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[18]  Konrad Schindler,et al.  Discrete-continuous optimization for multi-target tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Stan Birchfield.  Derivation of Kanade-Lucas-Tomasi Tracking Equation , 2006 .

[21]  Tsuhan Chen,et al.  Pictorial structures for object recognition and part labeling in drawings , 2011, 2011 18th IEEE International Conference on Image Processing.