“Particle Filter on GPUs for Real-Time Tracking” by Montemayor, Pantrigo, Sánchez and Fernández
Conference:
Type:
Entry Number: 094
Title:
- Particle Filter on GPUs for Real-Time Tracking
Presenter(s)/Author(s):
Abstract:
Efficient object tracking is required by many Computer Vision application areas like surveillance or robotics. It deals with statespace variables estimation of interesting features in image sequences and their future prediction. Probabilistic algorithms has been widely applied to tracking. These methods take advantage of knowledge about previous states of the system reducing the computational cost of an exhaustive search over the whole image.
References:
1. Arulampalam, S., Maskell, S., Gordon, N., and Clapp, T. 2002. A tutorial on particle filters for on-line nonlinear/non-gaussian bayesian tracking. IEEE Trans. on Signal Processing 50, 2, 174–188.
2. Deutscher, J., Blake, A., and Reid, I. 2000. Articulated body motion capture by annealed particle filtering. In Proc. of the IEEE Conf. on CVPR, vol. 2, 126–133.
3. Oh, K.-S., and Jung, K. 2004. Gpu implementation of neural networks. Pattern Recognition 37, 1311–1314.
