Foreground Extraction for Moving RGBD Cameras

In this paper, we propose a simple method to perform foreground extraction for a moving RGBD camera. These cameras have now been available for quite some time. Their popularity is primarily due to their low cost and ease of availability. Although the field of foreground extraction or background subtraction has been explored by the computer vision researchers since a long time, the depth-based subtraction is relatively new and has not been extensively addressed as of yet. Most of the current methods make heavy use of geometric reconstruction, making the solutions quite restrictive. In this paper, we make a novel use RGB and RGBD data: from the RGB frame, we extract corner features (FAST) and then represent these features with the histogram of oriented gradients (HoG) descriptor. We train a non-linear SVM on these descriptors. During the test phase, we make used of the fact that the foreground object has distinct depth ordering with respect to the rest of the scene. That is, we use the positively classified FAST features on the test frame to initiate a region growing to obtain the accurate segmentation of the foreground object from just the RGBD data. We demonstrate the proposed method of a synthetic datasets, and demonstrate encouraging quantitative and qualitative results.