FMODetect: Robust Detection of Fast Moving Objects

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00546470" target="_blank" >RIV/67985556:_____/21:00546470 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/21:00354083
Result on the web
<a href="http://dx.doi.org/10.1109/ICCV48922.2021.00352" target="_blank" >http://dx.doi.org/10.1109/ICCV48922.2021.00352</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCV48922.2021.00352" target="_blank" >10.1109/ICCV48922.2021.00352</a>

Result language
angličtina
Original language name
FMODetect: Robust Detection of Fast Moving Objects
Original language description
We propose the first learning-based approach for fast moving objects detection. Such objects are highly blurred and move over large distances within one video frame. Fastnmoving objects are associated with a deblurring and matting problem, also called deblatting. We show that the separation of deblatting into consecutive matting and deblurring allows achieving real-time performance, i.e. an order of magnitude speed-up, and thus enabling new classes of application. The proposed method detects fast moving objects as a truncated distance function to the trajectory by learning from synthetic data. For the sharp appearance estimation and accurate trajectory estimation, we propose a matting and fitting network that estimates the blurred appearance without background, followed by an energy minimization based deblurring. The state-of-the-art methods are outperformed in terms of recall, precision, trajectory estimation, and sharp appearance reconstruction. Compared to other methods, such as deblatting, the inference is of several orders of magnitude faster and allows applications such as real-time fast moving object detection and retrieval in large video collections.n
Czech name
—
Czech description
—

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
ISBN
978-1-6654-2812-5
ISSN
2380-7504
e-ISSN
2380-7504
Number of pages
9
Pages from-to
3541-3549
Publisher name
IEEE
Place of publication
Piscataway
Event location
Piscataway (on-line)
Event date
Oct 11, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)