Efficient Image Gradient-Based Object Localisation and Recognitio

Tan, T. N.; Sullivan, Geoffrey D.; Baker, Keith D.

doi:10.1109/CVPR.1996.517103

Efficient Image Gradient-Based Object Localisation and Recognitio

T. N. Tan, Geoffrey D. Sullivan, Keith D. Baker

CVPR 1996 pp. 397-402

doi:10.1109/CVPR.1996.517103 /cvpr/1996/tan1996cvpr-efficient/

Abstract

This paper reports novel algorithms for the efficient localisation and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. The algorithms make use of two a priori sources of knowledge about the scene and the objects: (i) the ground-plane constraint, and (ii) the fact that road vehicles are strongly rectilineal: The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles demonstrates the efficiency and robustness of context-based computer vision in road traffic scenes. The limitations of the algorithms are also addressed in the paper.

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Cite

Text

Tan et al. "Efficient Image Gradient-Based Object Localisation and Recognitio." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996. doi:10.1109/CVPR.1996.517103

Markdown

[Tan et al. "Efficient Image Gradient-Based Object Localisation and Recognitio." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1996.](https://mlanthology.org/cvpr/1996/tan1996cvpr-efficient/) doi:10.1109/CVPR.1996.517103

BibTeX

@inproceedings{tan1996cvpr-efficient,
  title     = {{Efficient Image Gradient-Based Object Localisation and Recognitio}},
  author    = {Tan, T. N. and Sullivan, Geoffrey D. and Baker, Keith D.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1996},
  pages     = {397-402},
  doi       = {10.1109/CVPR.1996.517103},
  url       = {https://mlanthology.org/cvpr/1996/tan1996cvpr-efficient/}
}