Modelling depth for nonparametric foreground segmentation using RGBD devices

TL;DR

This paper introduces a nonparametric RGBD foreground segmentation method that integrates multiple data cues, models depth data probabilistically, and demonstrates improved performance on a new dataset.

Contribution

A novel nonparametric approach that combines RGB and depth cues using a probabilistic depth model for enhanced foreground segmentation.

Findings

Effective handling of inaccurate depth data improves segmentation.

Proposed method outperforms existing approaches on the new dataset.

Introduces a new RGBD video dataset for benchmarking.

Abstract

The problem of detecting changes in a scene and segmenting the foreground from background is still challenging, despite previous work. Moreover, new RGBD capturing devices include depth cues, which could be incorporated to improve foreground segmentation. In this work, we present a new nonparametric approach where a unified model mixes the device multiple information cues. In order to unify all the device channel cues, a new probabilistic depth data model is also proposed where we show how handle the inaccurate data to improve foreground segmentation. A new RGBD video dataset is presented in order to introduce a new standard for comparison purposes of this kind of algorithms. Results show that the proposed approach can handle several practical situations and obtain good results in all cases.