Foreground detection on depth maps using skeletal representation of object silhouettes

Abstract

This article considers the problem of foreground detection on depth maps. The problem of finding objects of interest on images appears in many object detection, recognition and tracking applications as one of the first steps. However, this problem becomes too complicated for RGB images with multicolored or constantly changing background and in presence of occlusions. Depth maps provide valuable information about distance to the camera for each point of the scene, making it possible to explore object detection methods, based on depth features. We define foreground as a set of objects silhouettes, nearest to the camera relative to the local background. We propose a method of foreground detection on depth maps based on medial representation of objects silhouettes which does not require any machine learning procedures and is able to detect foreground in near real-time in complex scenes with occlusions, using a single depth map. Proposed method is implemented to depth maps, obtained from Kinect sensor.