多线索与局部遮挡处理的行人分类(MultiCue Pedestrian Classification with Partial Occlusion Handling).pdfVIP

多线索与局部遮挡处理的行人分类(Multi-Cue Pedestrian Classification with Partial Occlusion Handling) 数据介绍： Our training and test samples consist of manually labeled pedestrian and non-pedestrian bounding boxes in images captured from a vehicle-mounted calibrated stereo camera rig in an urban environment. For each manually labeled pedestrian, we created additional samples by geometric jittering. Non-pedestrian samples were the result of a shape detection pre-processing step with relaxed threshold setting, i.e. containing a bias towards more difficult patterns. 关键词： 样品,行人,非行人,前处理,阈值,matlab 程序, samples,pedestrian,non-pedestrian,pre-processing,threshold,matlab, 数据格式： TEXT 数据详细介绍： Multi-Cue Pedestrian Classification with Partial Occlusion Handling, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. Dataset Our training and test samples consist of manually labeled pedestrian and non-pedestrian bounding boxes in images captured from a vehicle-mounted calibrated stereo camera rig in an urban environment. For each manually labeled pedestrian, we created additional samples by geometric jittering. Non-pedestrian samples were the result of a shape detection pre-processing step with relaxed threshold setting, i.e. containing a bias towards more difficult patterns. Dense stereo is computed using the semi-global matching algorithm (H. Hirschmueller, Stereo processing by semi-global matching and mutual information, IEEE PAMI, 30(2):328-341, 2008) To compute dense optical flow, we use structure- and motion-adaptive regularized flow (A. Wedel et al., Structure- and motion-adaptive regularization for high accuracy optic flow, ICCV, 2009). Training and test samples have a resolution of 48 x 96 pixels with a 12-pixel border around the pedestrians. Note, that the experiments in our paper (see above) were done on 36 x 84 pixel images with a border of 6 pixels, i.e. crops of the provided d