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Daimler Multi-Cue, Occluded Pedestrian Classification Benchmark
创建来自Hello Dataset / Robert
概要
活动

Overview

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).

Data Format

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 dataset, with a three-component layout corresponding to head, torso, legs. For publication of the dataset, we chose to provide images with a larger border and without a pre-defined component layout, to allow for higher flexibility in the selection of components.

License

Custom

数据集信息
应用场景PersonAutonomous Driving
标注类型ClassificationOptical Flow
LicenseCustom
更新时间2021-03-24 22:54:07
数据概要
数据格式Image
数据数量0
文件大小8MB
标注数量0
版权归属方
Daimler AG
标注方
未知
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