Object Detection using the Kinect
Jason L. Owens · 2012
Abstract : We investigate an object detection system that uses both image and three-dimensional (3-D) point cloud data captured from the low-cost Microsoft Kinect vision sensor. The system works in three parts: image and point cloud data are fed into two components; the point cloud is segmented into hypothesized objects and the image region for those objects are extracted; and finally, a histogram of oriented gradient (HOG) descriptors are used for detection using a sliding window scheme. We evaluate this system by detecting backpacks on a challenging set of capture sequences in an indoor office environment with encouraging results.