RT Journal Article
T1 Real-time siamese multiple object tracker with enhanced proposals
A1 Vaquero Otal, Lorenzo
A1 Brea Sánchez, Víctor Manuel
A1 Mucientes Molina, Manuel
K1 Multiple visual object tracking
K1 Siamese CNN
K1 Motion estimation
AB Maintaining the identity of multiple objects in real-time video is a challenging task, as it is not always feasible to run a detector on every frame. Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information. To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION includes a novel proposal engine that produces quality features through an attention mechanism and a region-of-interest extractor fed by an inertia module and powered by a feature pyramid network. Finally, the extracted tensors enter a comparison head that efficiently matches pairs of exemplars and search areas, generating quality predictions via a pairwise depthwise region proposal network and a multi-object penalization module. SiamMOTION has been validated on five public benchmarks, achieving leading performance against current state-of-the-art trackers. Code available at: https://www.github.com/lorenzovaquero/SiamMOTION
PB Elsevier
YR 2023
FD 2023
LK http://hdl.handle.net/10347/29480
UL http://hdl.handle.net/10347/29480
LA eng
NO Pattern Recognition 135 (2023) 109141
NO This research was partially funded by the Spanish Ministerio de Ciencia e Innovación [grant numbers PID2020-112623GB-I00, RTI2018-097088-B-C32], and the Galician Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/29, ED431C 2021/048, ED431G 2019/04]. These grants are co-funded by the European Regional Development Fund (ERDF). Lorenzo Vaquero is supported by the Spanish Ministerio de Universidades under the FPU national plan (FPU18/03174). We also gratefully acknowledge the support of NVIDIA Corporation for hardware donations used for this research
DS Minerva
RD 24 abr 2026