RT Conference_Proceedings
T1 Spatio-temporal object detection from UAV on board cameras
A1 Cores Costa, Daniel
A1 Brea Sánchez, Víctor Manuel
A1 Mucientes Molina, Manuel
K1 object detection
K1 spatio-temporal features
K1 CNN
AB We propose a new two stage spatio-temporal object detector framework able to improve detection precision by taking into account temporal information. First, a short-term proposal linking and aggregation method improves box features. Then, we design a long-term attention module that further enhances short-term aggregated features addinglong-term spatio-temporal information. This module takes into account object trajectories to effectively exploit long-term relationships between proposals in arbitrary distant frames. Many videos recorded from UAV on board cameras have a high density of small objects, making the detection problem very challenging. Our method takes advantage of spatiotemporal information to address these issues increasing the detection robustness. We have compared our method with state-of-the-art video object detectors in two different publicly available datasets focused on UAV recorded videos. Our approach outperforms previous methods in both datasets.
YR 2021
FD 2021-09-27
LK http://hdl.handle.net/10347/32692
UL http://hdl.handle.net/10347/32692
LA eng
NO This research was partially funded by the Spanish Ministry of Science, Innovation and Universities under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32, and the Galician Ministry of Education, Culture and Universities under grants ED431C 2018/29, ED431C 2017/69 and accreditation 2016-2019, ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program).
DS Minerva
RD 28 abr 2026