Prediction based occluded multitarget tracking using spatio-temporal attention

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 20, Issue 6, 2006, Pages 925-938

  Lee, Heungkyu ^a   Kim, June ^a   Ko, Hanseok ^b  

^a Seokyeong University   (South Korea)

^b Korea University   (South Korea)

Author keywords

Focus of attention; Multitarget tracking; Occlusion; Spatial attention

Indexed keywords

ACCELERATION; COMPUTER SIMULATION; DEMODULATION; INFORMATION RETRIEVAL; ROBUSTNESS (CONTROL SYSTEMS); VECTORS; VELOCITY MEASUREMENT;

FOCUS OF ATTENTION; MULTITARGET TRACKING; OCCLUSION; SPATIAL ATTENTION;

PATTERN RECOGNITION;

EID: 33748988484     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001406005058     Document Type: Conference Paper

References (15)

1. Y. Bar-Shalom and X. R. Li, Multitarget-Multisensor Tracking: Principles and Techniques (YBS Press, 1995).
2. J. Bonaiuto and L. Itti, Using attention and spatial information for rapid facial recognition, Vid. Imag. Vis. Comput. 24(5) (2006) 1-9.
3. V. Cantoni, S. Levialdi and V. Roberto, Artificial Vision: Image Description, Recognition and Communication (Academic Press, 1997), pp. 3-64.
4. O. Hikosaka, S. Miyauchi and S. Shimojo, Focal visual attention produces illusory temporal order and motion sensation, Vis. Res. 33 (1993) 1219-1240.
5. L. Itti, C. Koch and E. Neibur, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans. Patt. Anal. Mach. Intell. 20(11) (1998) 1254-1259.
6. H. Lee and H. Ko, Occlusion activity detection algorithm using Kalman filter for detecting occluded multiple objects, Computational Science, Lecture Notes in Computer Science, Vol. 3514 (Springer, 2005), pp. 139-146.
7. H. Lee and H. Ko, Predictive estimation method to track occluded multiple objects using joint probabilistic data association filter, Image Analysis and Recognition, Lecture Notes in Computer Science, Vol. 3656 (Springer, 2005), pp. 852-860.
8. H. Lee and H. Ko, Spatio-temporal attention mechanism for more complex analysis to track multiple objects, Brain, Vision Artificial Intelligence, Lecture Notes in Computer Science, Vol. 3704 (Springer, 2005), pp. 447-456.
9. W. Li and E. Salari, Successive elimination algorithm for motion estimation, IEEE Trans. Imag. Process. 4 (1995) 105-107.
10. W. Lu and Y. P. Tan, A color histogram based people tracking system, in IEEE Int. Symp. Circuits and Systems (IEEE Press, May 2001), pp. 137-140.
11. S. McKenna and S. Jabri et al., Tracking groups of people, Comput. Vis. Imag. Underst. 80 (2000) 42-56.
12. C. Rasmussen and G. D. Hager, Joint probabilistic techniques for tracking multi-part objects, in IEEE Int. Conf. Computer Vision and Pattern Recognition (IEEE Press, 1998), pp. 16-21.
13. R. Rosales and S. Sclaroff, 3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions, in IEEE Int. Conf. Computer Vision and Pattern Recognition (IEEE Press, June 1999).
14. O. Stasse, Y. Kuniyoshi and G. Cheng, Development of a Biologically Inspired Real-Time Visual Attention System, Lecture Notes in Computer Science, Vol. 1811 (2000), pp. 150-159.
15. J. K. Tsotsos, The Selective Tuning Model, Visual Attention Mechanisms, eds. V. Cantoni, M. Marinaro and A. Petrosino (Kluwer Academic/Plenum Publishers, 2003), pp. 239-250.