Image Quality Assessment for Object Detection Performance in Surveillance Videos

Poonam Beniwal, Pranav Mantini, Shishir Shah

2023

Abstract

The proliferation of video surveillance cameras in recent years has increased the volume of visual data produced. This exponential growth in data has led to greater use of automated analysis. However, the performance of such systems depends upon the image/video quality, which varies heavily in the surveillance network. Compression is one such factor that introduces artifacts in the data. It is crucial to assess the quality of visual data to determine the reliability of the automated analysis. However, traditional image quality assessment (IQA) methods focus on the human perspective to objectively determine the quality of images. This paper focuses on assessing the image quality for the object detection task. We propose a full-reference quality metric based on the cosine similarity between features extracted from lossless compressed and lossy compressed images. However, the use of full-reference metrics is limited by the availability of reference images. To overcome this limitation, we also propose a no-reference metric. We evaluated our metric on a video surveillance dataset. The proposed quality metrics are evaluated using error vs. reject curves, demonstrating a better correlation with false negatives.

Paper Citation

in Harvard Style

Beniwal P., Mantini P. and Shah S. (2023). Image Quality Assessment for Object Detection Performance in Surveillance Videos. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 345-354. DOI: 10.5220/0011697300003417

in Bibtex Style

@conference{visapp23,
author={Poonam Beniwal and Pranav Mantini and Shishir Shah},
title={Image Quality Assessment for Object Detection Performance in Surveillance Videos},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={345-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011697300003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Image Quality Assessment for Object Detection Performance in Surveillance Videos
SN - 978-989-758-634-7
AU - Beniwal P.
AU - Mantini P.
AU - Shah S.
PY - 2023
SP - 345
EP - 354
DO - 10.5220/0011697300003417
PB - SciTePress