Generic Object Crowd Tracking by Multi-Task Learning

We address Multiple Object Tracking (MOT) in crowds, where the type of target objects is generic and not limited to pedestrians as in most previous work. Following the popular tracking-by-detection strategy, we decompose this problem into two main tasks, detection and tracking, and formulate them under the Multiple Task Learning (MTL) framework. A binary detector is learnt to detect objects in images, whilst multiple trackers are learnt on top of the detector by MTL to trace detected objects in subsequent frames. The detector is utilised to anchor the trackers, helping them not drift away from targets. The trackers are jointly learnt by sharing common features. To further improve the performance, we use a smoothness term which considers all labelled and unlabelled data globally. Experiments on challenging new generic object sequences as well as a publicly available sequence show that the proposed method significantly outperforms the state-of-the-art methods.

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

[2]  Zhen Qin,et al.  Improving multi-target tracking via social grouping , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Eric P. Xing,et al.  Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity , 2009, ICML.

[4]  Roberto Cipolla,et al.  Unsupervised Bayesian Detection of Independent Motion in Crowds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Narendra Ahuja,et al.  Robust Visual Tracking via Structured Multi-Task Sparse Learning , 2012, International Journal of Computer Vision.

[6]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Rich Caruana,et al.  Multitask Learning , 1997, Machine-mediated learning.

[8]  Antonio Torralba,et al.  Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[11]  Gérard G. Medioni,et al.  Tracking Using Motion Patterns for Very Crowded Scenes , 2012, ECCV.

[12]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[13]  Bo Geng,et al.  Manifold Regularized Multi-task Learning for Semi-supervised Multi-label Image Classification , 2013 .

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

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

[16]  Ramakant Nevatia,et al.  Learning affinities and dependencies for multi-target tracking using a CRF model , 2011, CVPR 2011.

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

[18]  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.

[19]  Takahiro Okabe,et al.  Using individuality to track individuals: Clustering individual trajectories in crowds using local appearance and frequency trait , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Ramakant Nevatia,et al.  Multi-target tracking by online learning of non-linear motion patterns and robust appearance models , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[22]  Shihong Lao,et al.  Group Tracking: Exploring Mutual Relations for Multiple Object Tracking , 2012, ECCV.

[23]  Thomas Deselaers,et al.  What is an object? , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[25]  Xi Chen,et al.  Accelerated Gradient Method for Multi-task Sparse Learning Problem , 2009, 2009 Ninth IEEE International Conference on Data Mining.

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

[27]  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.

[28]  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.

[29]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[30]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[31]  Jieping Ye,et al.  Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.

[32]  Ian D. Reid,et al.  Stable multi-target tracking in real-time surveillance video , 2011, CVPR 2011.

[33]  Mubarak Shah,et al.  (MP)2T: Multiple People Multiple Parts Tracker , 2012, ECCV.

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

[35]  Xiaoqin Zhang,et al.  Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[37]  Silvio Savarese,et al.  Multiple Target Tracking in World Coordinate with Single, Minimally Calibrated Camera , 2010, ECCV.

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

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

[40]  Ramakant Nevatia,et al.  Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[41]  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).

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

[43]  Yong Luo,et al.  Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification , 2013, IEEE Transactions on Image Processing.

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

[45]  Bernt Schiele,et al.  Multiple Object Class Detection with a Generative Model , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[46]  Jieping Ye,et al.  An accelerated gradient method for trace norm minimization , 2009, ICML '09.

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

[48]  Y. Nesterov.  Gradient methods for minimizing composite objective function , 2007 .

[49]  Xi Chen,et al.  Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso , 2010, ArXiv.

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

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