Re-identification for Online Person Tracking by Modeling Space-Time Continuum
暂无分享,去创建一个
Venu Govindaraju | Srirangaraj Setlur | Neeti Narayan | Nishant Sankaran | V. Govindaraju | S. Setlur | Nishant Sankaran | N. Narayan
[1] Kaiqi Huang,et al. An Equalized Global Graph Model-Based Approach for Multicamera Object Tracking , 2017, IEEE Transactions on Circuits and Systems for Video Technology.
[2] Xiaogang Wang,et al. Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[3] Shihong Lao,et al. Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[4] J. L. Wayman,et al. Best practices in testing and reporting performance of biometric devices. , 2002 .
[5] Francesco Solera,et al. Performance Measures and a Data Set for Multi-target, Multi-camera Tracking , 2016, ECCV Workshops.
[6] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[7] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[8] Luc Van Gool,et al. Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[9] Venu Govindaraju,et al. Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[10] Silvio Savarese,et al. Learning to Track at 100 FPS with Deep Regression Networks , 2016, ECCV.
[11] Silvio Savarese,et al. Learning to Track: Online Multi-object Tracking by Decision Making , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[12] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[13] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[14] Arun Ross,et al. Modelling errors in a biometric re-identification system , 2015, IET Biom..
[15] Mubarak Shah,et al. Floor Fields for Tracking in High Density Crowd Scenes , 2008, ECCV.
[16] Luc Van Gool,et al. Coupled Detection and Trajectory Estimation for Multi-Object Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[17] Amit K. Roy-Chowdhury,et al. A Camera Network Tracking (CamNeT) Dataset and Performance Baseline , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.
[18] Silvio Savarese,et al. Social LSTM: Human Trajectory Prediction in Crowded Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Ram Nevatia,et al. Learning to associate: HybridBoosted multi-target tracker for crowded scene , 2009, CVPR.
[20] Ramakant Nevatia,et al. Robust Object Tracking by Hierarchical Association of Detection Responses , 2008, ECCV.
[21] Roy L. Streit,et al. Maximum likelihood method for probabilistic multihypothesis tracking , 1994, Defense, Security, and Sensing.
[22] Shuicheng Yan,et al. An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[23] C. Jauffret,et al. A formulation of multitarget tracking as an incomplete data problem , 1997, IEEE Transactions on Aerospace and Electronic Systems.
[24] Hai Tao,et al. Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features , 2008, ECCV.
[25] Dani Lischinski,et al. Crowds by Example , 2007, Comput. Graph. Forum.
[26] Xiaogang Wang,et al. Visual Tracking with Fully Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[27] Luc Van Gool,et al. Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings , 2010, ECCV.
[28] Chi Zhang,et al. Cross Domain Knowledge Transfer for Person Re-identification , 2016, ArXiv.
[29] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.