LGT/VOT tracking performance evaluation of depth images
Hussein Haggag, Mohammed Hossny, Sherif Haggag, Jingjing Xiao, Saeid Nahavandi, Doug Creighton · 2014
This paper presents object tracking in depth, RGB and normal-maps images using LGT tracker. The depth and RGB images are rendered using depth imaging plugins. A series of experiments were held to evaluate the tracker performance in tracking objects in different image sequences. The experiments conducted were from the Visual Object Tracking (VOT) challenge that was arranged in association with ICCV'13 The accuracy was chosen as the evaluation measure, where the the tracker's bounding box was compared against the ground truth bounding box. Results show that tracking object using depth images gives better results and is more accurate than tracking using either the RGB or nomal maps images.