Multi-object tracking based on particle filter and data association in color image sequences

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5337 LNCS, Issue , 2009, Pages 133-142

  Al Hamadi, Ayoub ^a   Pathan, Saira Saleem ^a   Michaelis, Bernd ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

Object Tracking; Particle Filter; Pattern Recognition

Indexed keywords

ADAPTIVE MOTION; COLOR IMAGE SEQUENCES; DATA ASSOCIATION; IMAGE SEQUENCE; INTEGRATED DATA; MOVING OBJECTS; MULTI-OBJECT TRACKING; MULTI-OBJECTS; MULTIPLE OBJECTS; NON COOPERATIVE SITUATIONS; NOVEL METHODS; OBJECT TRACKING; PARTICLE FILTER; ROBUST TRACKING;

AIR FILTERS; COLOR IMAGE PROCESSING; COMPUTER VISION; DISTRIBUTED COMPUTER SYSTEMS; TARGET TRACKING;

NONLINEAR FILTERING;

EID: 68749096372     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02345-3_14     Document Type: Conference Paper

References (13)

1. Isard, M., Blake, A.: ICondensation: Unifying low-level and high-level tracking in a stochastic framework. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 893-908. Springer, Heidelberg (1998)
2. Sidenbladh, H.: Multi-target particle filtering for the probability hypothesis density. In: 6th International Conf. on Information Fusion, Cairns, vol. 2, pp. 800-806 (2003)
3. Chang, C., Ansari, R.: Kernel Particle Filter for Visual Tracking. IEEE Signal Processing Letters 12(3), 242-245 (2006)
4. Doucet, A., de Freitas, N., Murphy, K., Russell, S.: Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. In: Conference on Uncertainty in Artificial Intelligence, pp. 176-183 (2000)
5. Murphy, K., Russel, S.: Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks: Sequential Monte Carlo Methods in Practice, ch. 24, pp. 499-515. Springer, Heidelberg (2001)
6. Casella, G., Robert, C.: Rao-Blackwellisation of Sampling Schemes. Biometrika, Heft 83(1), 81-94 (1996)
7. Vermaak, J., Doucet, A., Pérez, P.: Maintaining Multi-Modality through Mixture Tracking. In: IEEE Intern. Conf. on Computer Vision, vol. 2, pp. 1110-1116 (2003)
8. Vermaak, J., Gidsill, S., Pérez, P.: Monte Carlo filtering for multi-target tracking and data association. IEEE Trans. on AES, Heft 41(1), 309-332 (2005)
9. Cox, I., Hingorani, S.: An Efficient Implementation and Evaluation of Reid's Multiple Hypothesis Tracking Algorithm for Visual Tracking. In: Proc. of the 12th ICPR, vol. 1, pp. 437-442 (1994)
10. Kreucher, C., Hero, A., Kastella, K.: Multiple Model Particle Filtering for Multi-target Tracking. In: Proc. of the 12th ASAP (2004)
11. Karlsson, R., Gustafsson, F.: Entropy, Monte Carlo data association for multiple target tracking. In: IEE Internat. Seminar on Target Tracking: Algorithms and Applications, vol. 1, pp. 13/1-13/5 (2001)
12. Al-Hamadi, A., Michaelis, B.: An intelligent paradigm for multi-objects tracking in crowded environment. Journal-JDIM 4(3), 183-190 (2006)
13. Helmick, R.: IMM Estimator with Nearest-Neighbor Joint Probabilistic Data Association. In: Bar-Shalom, Y. (ed.) Multitarget-Multisensor Tracking: Applications and Advances, ch. 3, pp. 161-198. AH Publishers, Boston (2000)