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Daimler Multi-Cue, Occluded Pedestrian Classification Benchmark
update dataset overview and ba...
Feb 10, 2022 7:38 AM


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.

🎉Many thanks to Graviti Open Datasets for contributing the dataset
Basic Information
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Updated on2022-02-10 07:38:04
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Copyright Owner
Daimler AG