Overview

We propose a new dataset that is used for benchmarking the accuracy of a real-time object detector (Faster R-CNN). Part of the data was collected using an HD camera mounted on a vehicle. Furthermore, some of the data is weakly annotated so it can be used for testing weakly supervised learning techniques. There already exist urban object datasets, but none of them include all the essential urban objects. We carried out extensive experiments demonstrating the effectiveness of the baseline approach. Additionally, we propose an R-CNN plus tracking technique to accelerate the process of real-time urban object detection.

Citation

@article{dominguez2018new,
  title={A new dataset and performance evaluation of a region-based cnn for urban object detection},
  author={Dominguez-Sanchez, Alex and Cazorla, Miguel and Orts-Escolano, Sergio},
  journal={Electronics},
  volume={7},
  number={11},
  pages={301},
  year={2018},
  publisher={Multidisciplinary Digital Publishing Institute}
}