Efficient Object Annotation for Surveillance and Automotive Applications
Abstract
Accurately annotated large video data is critical for the development of reliable surveillance and automotive related vision solutions. In this work, we propose an efficient and yet accurate annotation scheme for objects in videos (pedestrians in this case) with minimal supervision. We annotate objects with tight bounding boxes. We propagate the annotations across the frames with a self training based approach. An energy minimization scheme for the segmentation is the central component of our method. Unlike the popular grab cut like segmentation schemes, we demand minimal user intervention. Since our annotation is built on an accurate segmentation, our bounding boxes are tight. We validate the performance of our approach on multiple publicly available datasets.
Cite
Text
Swetha et al. "Efficient Object Annotation for Surveillance and Automotive Applications." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2016. doi:10.1109/WACVW.2016.7470117Markdown
[Swetha et al. "Efficient Object Annotation for Surveillance and Automotive Applications." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2016.](https://mlanthology.org/wacvw/2016/swetha2016wacvw-efficient/) doi:10.1109/WACVW.2016.7470117BibTeX
@inproceedings{swetha2016wacvw-efficient,
title = {{Efficient Object Annotation for Surveillance and Automotive Applications}},
author = {Swetha, Sirnam and Mishra, Anand and Hegde, Guruprasad M. and Jawahar, C. V.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision Workshops},
year = {2016},
pages = {1-6},
doi = {10.1109/WACVW.2016.7470117},
url = {https://mlanthology.org/wacvw/2016/swetha2016wacvw-efficient/}
}