Unsupervised Processing of Vehicle Appearance for Automatic Understanding in Traffic Surveillance [DICTA 2015]

Jakub Sochor and Adam Herout
GRAPH@FIT, Brno University of Technology
Corresponding authors: isochor [at] fit.vutbr.cz

Abstract

This paper deals with unsupervised collection of information from traffic surveillance video streams. Deployment of usable traffic surveillance systems requires minimizing of efforts per installed camera – our goal is to enroll a new view on the street without any human operator input. We propose a method of automatically collecting vehicle samples from surveillance cameras, analyze their appearance and fully automatically collect a fine-grained dataset. This dataset can be used in multiple ways, we are explicitly showcasing the following ones: fine-grained recognition of vehicles and camera calibration including the scale. The experiments show that based on the automatically collected data, make&model vehicle recognition in the wild can be done accurately: average precision 0.890. The camera scale calibration (directly enabling automatic speed and size measurement) is twice as precise as the previous existing method. Our work leads to automatic collection of traffic statistics without the costly need for manual calibration or make&model annotation of vehicle samples. Unlike most previous approaches, our method is not limited to a small range of viewpoints (such as eye-level cameras shots), which is crucial for surveillance applications.

Downloads

  • Paper
  • Source code for the algorithm described in the paper. Please cite our paper if you use this code.
  • Dataset – 472k of vehicles, 3 images each (~1.4 milion of images, 58GB of data), multiple cameras, each annotated by bounding box. Please cite our paper if you use this dataset.

Citation

@INPROCEEDINGS{Sochor2015, 
author={Sochor, Jakub and Herout, Adam}, 
booktitle={Digital Image Computing: Techniques and Applications (DICTA), 2015 International Conference on}, 
title={Unsupervised Processing of Vehicle Appearance for Automatic Understanding in Traffic Surveillance}, 
year={2015}, 
pages={1-8}, 
month={Nov},
}

Teaser

Acknowledgements

This work was supported by TACR project “RODOS”, TE01020155 and by the research CEZMSMT project “IT4I”, CZ 1.05/1.1.00/02.0070