TY - GEN
T1 - Assessing the Efficiency of One-shot Visual Object Trackers for Underwater Robot Position Locking
AU - Aman, Waqas
AU - Al-Zawqari, Ali
AU - Mohsen, Farida
AU - Al-Kuwari, Saif
AU - Safa, Ali
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/10/22
Y1 - 2025/10/22
N2 - This study presents, to our knowledge, the first comprehensive evaluation of seven Machine Learning (ML)based one-shot object tracking algorithms for the vision-based position stabilization of remotely-operated underwater vehicles (ROVs). We introduce a position-locking framework that analyzes images of a target object, in front of which the ROV must maintain stability. The system leverages the outputs of various object-tracking algorithms to autonomously adjust the ROV's position in response to external disturbances. Extensive realworld experiments were conducted using a BlueROV2 platform in an indoor pool, highlighting the advantages and limitations of each tracking method. Additionally, to address the lack of publicly available underwater ROV datasets, we are releasing our collected data as open-source, aiming to support and advance future research in this field.
AB - This study presents, to our knowledge, the first comprehensive evaluation of seven Machine Learning (ML)based one-shot object tracking algorithms for the vision-based position stabilization of remotely-operated underwater vehicles (ROVs). We introduce a position-locking framework that analyzes images of a target object, in front of which the ROV must maintain stability. The system leverages the outputs of various object-tracking algorithms to autonomously adjust the ROV's position in response to external disturbances. Extensive realworld experiments were conducted using a BlueROV2 platform in an indoor pool, highlighting the advantages and limitations of each tracking method. Additionally, to address the lack of publicly available underwater ROV datasets, we are releasing our collected data as open-source, aiming to support and advance future research in this field.
KW - One-shot object tracking
KW - Robot position control
KW - Underwater robot navigation
UR - https://www.scopus.com/pages/publications/105032351038
U2 - 10.1109/AICCSA66935.2025.11315324
DO - 10.1109/AICCSA66935.2025.11315324
M3 - Conference contribution
AN - SCOPUS:105032351038
SN - 979-8-3315-5694-5
T3 - International Conference On Computer Systems And Applications
BT - 2025 Ieee/acs 22nd International Conference On Computer Systems And Applications, Aiccsa
PB - IEEE Computer Society
T2 - 22nd ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2025
Y2 - 19 October 2025 through 22 October 2025
ER -