Bi-label Propagation for Generic Multiple Object Tracking

In this paper, we propose a label propagation framework to handle the multiple object tracking (MOT) problem for a generic object type (cf. pedestrian tracking). Given a target object by an initial bounding box, all objects of the same type are localized together with their identities. We treat this as a problem of propagating bi-labels, i.e. a binary class label for detection and individual object labels for tracking. To propagate the class label, we adopt clustered Multiple Task Learning (cMTL) while enforcing spatio-temporal consistency and show that this improves the performance when given limited training data. To track objects, we propagate labels from trajectories to detections based on affinity using appearance, motion, and context. Experiments on public and challenging new sequences show that the proposed method improves over the current state of the art on this task.

[1]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[2]  Roberto Cipolla,et al.  Label propagation in video sequences , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Margrit Betke,et al.  Coupling detection and data association for multiple object tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[5]  Charles A. Micchelli,et al.  A Spectral Regularization Framework for Multi-Task Structure Learning , 2007, NIPS.

[6]  Afshin Dehghan,et al.  Part-based multiple-person tracking with partial occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Bi Song,et al.  A Stochastic Graph Evolution Framework for Robust Multi-target Tracking , 2010, ECCV.

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

[9]  Ramakant Nevatia,et al.  An online learned CRF model for multi-target tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[11]  Haroon Idrees,et al.  Detection and Tracking of Large Number of Targets in Wide Area Surveillance , 2010, ECCV.

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

[13]  Luis E. Ortiz,et al.  Who are you with and where are you going? , 2011, CVPR 2011.

[14]  Mubarak Shah,et al.  Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Ko Nishino,et al.  Tracking with local spatio-temporal motion patterns in extremely crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Haibin Ling,et al.  Multi-target Tracking by Rank-1 Tensor Approximation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Ram Nevatia,et al.  Learning to associate: HybridBoosted multi-target tracker for crowded scene , 2009, CVPR.

[18]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[19]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[21]  Ming Yang,et al.  Detection driven adaptive multi-cue integration for multiple human tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Jiayu Zhou,et al.  Clustered Multi-Task Learning Via Alternating Structure Optimization , 2011, NIPS.

[23]  Luc Van Gool,et al.  Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Wenhan Luo,et al.  Generic Object Crowd Tracking by Multi-Task Learning , 2013, BMVC.

[25]  Mohamed R. Amer,et al.  Multiobject tracking as maximum weight independent set , 2011, CVPR 2011.

[26]  Ramakant Nevatia,et al.  Multi-target tracking by on-line learned discriminative appearance models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Rui Caseiro,et al.  Globally optimal solution to multi-object tracking with merged measurements , 2011, 2011 International Conference on Computer Vision.

[28]  Massimiliano Pontil,et al.  Regularized multi--task learning , 2004, KDD.

[29]  François Fleuret,et al.  FlowBoost — Appearance learning from sparsely annotated video , 2011, CVPR 2011.

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

[31]  Robert T. Collins,et al.  Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.