This page contains two datasets with roughly 1000 images each and object bounding boxes for cars or pedestrians (~10000 bounding boxes in total). Furthermore, it contains the orientation of each object, discretized into 8 classes for cars and 4 classes for pedestrians. The .zip files provided below come with a MATLAB-based label viewer. Labels are saved as MATLAB matrices (.mat files), where each row is an object in the image and the columns are position, size, class and orientation of the object. You can use this dataset to train your preferred objected detector. We had very good experiences with the cascaded part-based L-SVM from Ross Girshick and Pedro Felzenszwalb, which we modifed to fix the latent orientations to the ones given by the labels. We have been able to detect very small objects by upsampling the original images up to a factor of three. Note: Also try our novel KITTI dataset here! All datasets on this page are published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. This dataset has been used for learning the part-based object detector in:
@inproceedings{Geiger2011NIPS,
  author = {Andreas Geiger and Christian Wojek and Raquel Urtasun},
  title = {Joint 3D Estimation of Objects and Scene Layout},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)},
  year = {2011}
}