RT Journal Article
T1 STDnet-ST: Spatio-temporal ConvNet for small object detection
A1 Bosquet Mera, Brais
A1 Mucientes Molina, Manuel
A1 Brea Sánchez, Víctor Manuel
K1 Small object detection
K1 Spatio-temporal convolutional network
K1 Object linking
AB Object detection through convolutional neural networks is reaching unprecedented levels of precision. However, a detailed analysis of the results shows that the accuracy in the detection of small objects is still far from being satisfactory. A recent trend that will likely improve the overall object detection success is to use the spatial information operating alongside temporal video information. This paper introduces STDnet-ST, an end-to-end spatio-temporal convolutional neural network for small object detection in video. We define small as those objects under  px, where the features become less distinctive. STDnet-ST is an architecture that detects small objects over time and correlates pairs of the top-ranked regions with the highest likelihood of containing those small objects. This permits to link the small objects across the time as tubelets. Furthermore, we propose a procedure to dismiss unprofitable object links in order to provide high quality tubelets, increasing the accuracy. STDnet-ST is evaluated on the publicly accessible USC-GRAD-STDdb, UAVDT and VisDrone2019-VID video datasets, where it achieves state-of-the-art results for small objects
PB Elsevier
SN 0031-3203
YR 2021
FD 2021
LK http://hdl.handle.net/10347/28783
UL http://hdl.handle.net/10347/28783
LA eng
NO Pattern Recognition 116 (2021) 107929
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