Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors

Abstract

The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has recently attracted a lot of interest in the tracking community mainly for its linear complexity and its ability to deal with high clutter especially in radar/sonar scenarios. In the computer vision community however, underlying constraints are different from radar scenarios and have to be taken into account when using the PHD filter. In this article, we propose a new tree-based path extraction algorithm for a Gaussian Mixture PHD filter in Computer Vision applications. We also investigate how an additional benefit can be achieved by using a second human detector and justify an approximation for multiple sensors in low-clutter scenarios.