Improving Video Object Detection by Seq-Bbox Matching

Hatem Belhassen, Heng Zhang, Virginie Fresse, El-Bay Bourennane

2019

Abstract

Video object detection has drawn more and more attention in recent years. Compared with object detection from image, object detection in video is more useful in many practical applications, e.g. self-driving cars, smart video surveillance, etc. It is highly required to build a fast, reliable and low-cost video-based object detection system for these applications. In this work, we propose a novel, simple and highly effective box-level post-processing method to improve the accuracy of video object detection. The proposed method is based on both online and an offline settings. Our experiments on ImageNet object detection from video (VID) dataset show that our method brings important accuracy gains, especially to more challenging fast-moving object detection, with quite light computational overhead in both settings. Applied to YOLOv3, our system achieves so far the best speed/accuracy trade-off for offline video object detection and competitive detection improvements for online object detection.

Paper Citation

in Harvard Style

Belhassen H., Zhang H., Fresse V. and Bourennane E. (2019). Improving Video Object Detection by Seq-Bbox Matching. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 226-233. DOI: 10.5220/0007260002260233

in Bibtex Style

@conference{visapp19,
author={Hatem Belhassen and Heng Zhang and Virginie Fresse and El-Bay Bourennane},
title={Improving Video Object Detection by Seq-Bbox Matching},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={226-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007260002260233},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Improving Video Object Detection by Seq-Bbox Matching
SN - 978-989-758-354-4
AU - Belhassen H.
AU - Zhang H.
AU - Fresse V.
AU - Bourennane E.
PY - 2019
SP - 226
EP - 233
DO - 10.5220/0007260002260233
PB - SciTePress