TY - JOUR
T1 - Practical Tracking Method based on Best Buddies Similarity
AU - He, Haiyu
AU - Chen, Zhen
AU - Liu, Haikuo
AU - Liu, Xiangdong
AU - Guo, Youguang
AU - Li, Jian
N1 - Publisher Copyright:
Copyright © 2023 Haiyu He et al.
PY - 2023
Y1 - 2023
N2 - Visual tracking is a crucial skill for bionic robots to perceive the environment and control their movement. However, visual tracking is challenging when the target undergoes nonrigid deformation because of the perspective change from the camera mounted on the robot. In this paper, a real-time and scale-adaptive visual tracking method based on best buddies similarity (BBS) is presented, which is a state-of-the-art template matching method that can handle nonrigid deformation. The proposed method improves the original BBS in 4 aspects: (a) The caching scheme is optimized to reduce the computational overhead, (b) the effect of cluttered backgrounds on BBS is theoretically analyzed and a patch-based texture is introduced to enhance the robustness and accuracy, (c) the batch gradient descent algorithm is used to further speed up the method, and (d) a resample strategy is applied to enable the BBS to track the target in scale space. The proposed method on challenging real-world datasets is evaluated and its promising performance is demonstrated.
AB - Visual tracking is a crucial skill for bionic robots to perceive the environment and control their movement. However, visual tracking is challenging when the target undergoes nonrigid deformation because of the perspective change from the camera mounted on the robot. In this paper, a real-time and scale-adaptive visual tracking method based on best buddies similarity (BBS) is presented, which is a state-of-the-art template matching method that can handle nonrigid deformation. The proposed method improves the original BBS in 4 aspects: (a) The caching scheme is optimized to reduce the computational overhead, (b) the effect of cluttered backgrounds on BBS is theoretically analyzed and a patch-based texture is introduced to enhance the robustness and accuracy, (c) the batch gradient descent algorithm is used to further speed up the method, and (d) a resample strategy is applied to enable the BBS to track the target in scale space. The proposed method on challenging real-world datasets is evaluated and its promising performance is demonstrated.
UR - http://www.scopus.com/inward/record.url?scp=85174005202&partnerID=8YFLogxK
U2 - 10.34133/cbsystems.0050
DO - 10.34133/cbsystems.0050
M3 - Article
AN - SCOPUS:85174005202
SN - 2097-1087
VL - 4
JO - Cyborg and Bionic Systems
JF - Cyborg and Bionic Systems
M1 - 0050
ER -