Robust visual tracking based on adaptive depth-color-cue integration using range sensor

IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

Volumn , Issue , 2012, Pages 330-335

  Wang, Can ^a   Liu, Hong ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND REGION; ILLUMINATION CHANGES; INTEGRATION FRAMEWORKS; MEAN SHIFT; MOTION CUES; MOTION DETECTION; RANGE SENSORS; RELIABILITY AND ROBUSTNESS; RELIABILITY EVALUATION; TARGET REGIONS; VISUAL TRACKING;

INTELLIGENT SYSTEMS; PROBABILITY DISTRIBUTIONS; TRACKING (POSITION);

COLOR;

EID: 84870578181     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MFI.2012.6342998     Document Type: Conference Paper

References (21)

1. Eric Hayman, Jan-Olof Eklundh, "Probabilistic and voting approaches to cue integration for figure-ground segmentation", in IEEE ECCV, 2002, pp.469-486.
2. Vermaak, J., Perez, P., Gangnet, M. and Blake, A., "Towards improved observation models for visual tracking: Selective adaptation", in IEEE ECCV, pp.645-660.
3. H. Liu and H. He, "A salient feature and scene semantics based attention model for human tracking on mobile robot ", in IEEE ICRA, 2010, pp. 4545-4552.
4. H. Liu, Z. Yu, H. Zha and Y. Zou, "Robust human tracking based on multi-cue integration and mean Sshift ", Pattern Recognition Letters, April 2009, pp. 827-837.
5. Knoop, S, Vacek, S and Dillmann, R, "Sensor fusion for 3D human body tracking with an articulated 3D body model ", in IEEE ICRA, 2006, pp.58-69.
6. J. Giebel, D. M. Gavrila and C. Schnfirr, "A bayesian framework for multi-cue 3D object tracking computer vision", in IEEE ECCV Lecture Notes in Computer Science, 2004, pp.241-252.
7. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking" IEEE Transaction on Signal Process, 2002, pp.174-188.
8. Q. Yu, G. Medioni and I. Cohen, "Multiple target tracking using spatio-temporal markov chain monte carlo data association", in IEEE CVPR, 2007, pp. 1-8.
9. Breitenstein M.D., Reichlin D. Koller and Leibe B., "Robust trackingby-detection using a detector confidence particle filter", in IEEE ICCV, 2009, pp. 1515-1522.
10. Yalcm, I.K. and Gokmen, M., "Visual tracking via geometric particle filtering on the affine group with optimal importance functions", in IEEE CVPR, 2009, pp. 991-998.
11. Y. Rathi and N. Vaswani, "Tracking deforming objects using particle filtering for geometric active contours", in IEEE PAMI, 2007, pp. 1470-1475.
12. Comaniciu, D., Ramesh, V. and Meer, P., "Kernel-based object tracking", in IEEE PAMI, 2003, pp. 564-577.
13. R. Stolkin, I. Florescu, G. Kamberov. "An adaptive background model for CAMSHIFT tracking with a movingcamera". in International Conference on Advances in Pattern Recognition, 2007, pp. 5.
14. R. Stolkin, "Continuous Machine Learning in Computer Vision-Tracking with Adaptive Class Models", in Advanced Robotic Systems International, 2007, pp. s265-282.
15. T.L. Liu, "Real-Time Tracking using Trust-Region Methods", in IEEE TPAMI, 2004, pp. s397-402.
16. X. Song, X. Shao, R. Shibasaki, H. Zhao, J. Cui, and H. Zha, "A novel laser-based system: Fully online detection of abnormal activity via an unsupervised method, "in IEEE ICRA, 2011, pp. 1317-1322s.
17. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, "Real-time human pose recognition in parts from single depth images", in IEEE CVPR, 2011, pp. 1297-1304.
18. C. Plagemann, V. Ganapathi, D. Koller, and S. Thrun, "Real-time identification and localization of body parts from depth images", in IEEE ICRA, 2010, pp. 3108-3113.
19. Microsoft Corp. and Redmond WA., "Kinect for xbox 360".
20. Ning, J.; Zhang, L.; Zhang, D.; Wu, C. "Scale and orientation adaptive mean shift tracking", in IET Comput. Vis., 2011, pp. 52-61.
21. Z. Kalal, K. Mikolajczyk, and J. Matas, "Tracking-Learning- Detection," IEEE TPAMI, 2011, pp. 3789-3792.