Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones
Abstract
Moving object detection is an imperative task in computer vision, where it is primarily used for surveillance applications. With the increasing availability of low-altitude aerial vehicles, new challenges for moving object detection have surfaced, both for academia and industry. In this paper, we propose a new approach that can detect moving objects efficiently and handle parallax cases. By introducing sparse flow based parallax handling and downscale processing, we push the boundaries of real-time performance with 16 FPS on limited embedded resources (a five-fold improvement over existing baselines), while managing to perform comparably or even improve the state-of-the-art in two different datasets. We also present a roadmap for extending our approach to exploit multi-modal data in order to mitigate the need for parameter tuning.
Cite
Text
Lezki et al. "Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11012-3_8Markdown
[Lezki et al. "Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/lezki2018eccvw-joint/) doi:10.1007/978-3-030-11012-3_8BibTeX
@inproceedings{lezki2018eccvw-joint,
title = {{Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones}},
author = {Lezki, Hazal and Ozturk, Ahu and Akpinar, M. Akif and Yucel, Mehmet Kerim and Logoglu, K. Berker and Erdem, Aykut and Erdem, Erkut},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {100-116},
doi = {10.1007/978-3-030-11012-3_8},
url = {https://mlanthology.org/eccvw/2018/lezki2018eccvw-joint/}
}