ML Anthology

Efficient Scene Layout Aware Object Detection for Traffic Surveillance

Tao Wang, Xuming He, Songzhi Su, Yin Guan
CVPRW 2017 pp. 926-933
doi:10.1109/CVPRW.2017.128 /cvprw/2017/wang2017cvprw-efficient/

Abstract

We present an efficient scene layout aware object detection method for traffic surveillance. Given an input image, our approach first estimates its scene layout by transferring object annotations in a large dataset to the target image based on nonparametric label transfer. The transferred annotations are then integrated with object hypotheses generated by the state-of-the-art object detectors. We propose an approximate nearest neighbor search scheme for efficient inference in the scene layout estimation. Experiments verified that this simple and efficient approach provides consistent performance improvements to the state-of-the-art object detection baselines on all object categories in the TSWC-2017 localization challenge.

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Cite

Text

Wang et al. "Efficient Scene Layout Aware Object Detection for Traffic Surveillance." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.128

Markdown

[Wang et al. "Efficient Scene Layout Aware Object Detection for Traffic Surveillance." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/wang2017cvprw-efficient/) doi:10.1109/CVPRW.2017.128

BibTeX

@inproceedings{wang2017cvprw-efficient,
  title     = {{Efficient Scene Layout Aware Object Detection for Traffic Surveillance}},
  author    = {Wang, Tao and He, Xuming and Su, Songzhi and Guan, Yin},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {926-933},
  doi       = {10.1109/CVPRW.2017.128},
  url       = {https://mlanthology.org/cvprw/2017/wang2017cvprw-efficient/}
}