Person tracking-by-detection with efficient selection of part-detectors

In this paper we introduce a new person tracking-by-detection approach based on a particle filter. We leverage detection and appearance cues and apply explicit occlusion reasoning. The approach samples efficiently from a large set of available person part-detectors in order to increase runtime performance while retaining accuracy. The tracking approach is evaluated and compared to the state of the art on the CAVIAR surveillance dataset as well as on a multimedia dataset consisting of six episodes of the TV series The Big Bang Theory. The results demonstrate the versatility of the approach on very different types of data and its robustness to camera movement and non-pedestrian body poses.

[1]  Michael Arens,et al.  Detection and tracking of objects with direct integration of perception and expectation , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[2]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

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

[4]  Stefan Roth,et al.  People-tracking-by-detection and people-detection-by-tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Ramakant Nevatia,et al.  Robust Object Tracking by Hierarchical Association of Detection Responses , 2008, ECCV.

[6]  Jitendra Malik,et al.  Poselets: Body part detectors trained using 3D human pose annotations , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

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

[9]  Ramakant Nevatia,et al.  Online Learned Discriminative Part-Based Appearance Models for Multi-human Tracking , 2012, ECCV.

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

[11]  Ramakant Nevatia,et al.  Robust multi-pose face tracking by multi-stage tracklet association , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).