Improving a Real-Time Object Detector with Compact Temporal Information
Abstract
Neural networks designed for real-time object detection have recently improved significantly, but in practice, looking at only a single RGB image at the time may not be ideal. For example, when detecting objects in videos, a foreground detection algorithm can be used to obtain compact temporal data, which can be fed into a neural network alongside RGB images. We propose an approach for doing this, based on an existing object detector, that re-uses pretrained weights for the processing of RGB images. The neural network was tested on the VIRAT dataset with annotations for object detection, a problem this approach is well suited for. The accuracy was found to improve significantly (up to 66%), with a roughly 40% increase in computational time.
Cite
Text
Ahrnbom et al. "Improving a Real-Time Object Detector with Compact Temporal Information." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.31Markdown
[Ahrnbom et al. "Improving a Real-Time Object Detector with Compact Temporal Information." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/ahrnbom2017iccvw-improving/) doi:10.1109/ICCVW.2017.31BibTeX
@inproceedings{ahrnbom2017iccvw-improving,
title = {{Improving a Real-Time Object Detector with Compact Temporal Information}},
author = {Ahrnbom, Martin and Jensen, Morten Borno and Åström, Kalle and Nilsson, Mikael G. and Ardö, Håkan and Moeslund, Thomas B.},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {190-197},
doi = {10.1109/ICCVW.2017.31},
url = {https://mlanthology.org/iccvw/2017/ahrnbom2017iccvw-improving/}
}