168 / 1971-01-01 00:00:00

Gaussian Mixture Probability Hypothesis Density Filter For Tracking Visual Targets

probability hypothesis density,feature measurement,visual tracking

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multiple targets filter based on random finite sets (RFS). It propagates the posterior intensity of the random sets of targets. In this paper, we apply the Gaussian Mixture (GM) PHD filter to track a random number of moving targets in visual sequences. To obtain the PHD of visual objects, we propose a method to approximate the posterior intensity using the feature measurement. Monte Carlo technology is adopted to obtain the feature measurement random set by sample particles with the integer label. And we adopt an adaptive weight to fuse the color and edge features to improve the represent ability of tracking targets. The experimental results have demonstrated the effectiveness of our method.