CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
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We propose a statistical motion model for sequential Bayesian tracking, called the optical flow-driven motion model, and show an adaptive particle filter algorithm with the motion model. It predicts the current state with the help of optical flows, i.e., it explores the state space with information based on the current and previous images of an image sequence. In addition, we introduce an automatic method for adjusting the variance of the motion model, which parameter is manually determined in most particle filters. In experiments with synthetic and real image sequences, we compare the proposed motion model with a random walk model, which is a widely used model for tracking, and show the proposed model outperform the random walk model in terms of accuracy even though their execution times are almost the same.