TY - GEN
T1 - An adaptive compressive tracking algorithm for amphibious spherical robots
AU - Pan, Shaowu
AU - Guo, Shuxiang
AU - Shi, Liwei
AU - Guo, Ping
AU - He, Yanlin
AU - Tang, Kun
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - As a critical important function for autonomous mobile robots, visual tracking is a challenge work in the field of computer vision, for the reason that factors like illumination variance, partial occlusions and target appearance changes shall be carefully considered. Focus on applications of our amphibious spherical robots, an adaptive visual tracking algorithm was proposed on the basis of compressive tracking. A feature selection method was designed to choose random Haar-like feature templates in various scales by calculating Fisher's criterion functions of features. On this basis, a random feature pool, which tried to preserve discriminative features at different frames, were constructed and then maintained on-line to provide candidate appearance model of the target. Moreover, an adaptive update mechanism was adopted for selectively updating feature templates and classifier parameters of the improved compressive tracking algorithm, which alleviated the drift problem. Experimental results with various image sequences demonstrated the effectiveness and robustness of the proposed tracking algorithm, which can meet practical application requirements of the amphibious spherical robots.
AB - As a critical important function for autonomous mobile robots, visual tracking is a challenge work in the field of computer vision, for the reason that factors like illumination variance, partial occlusions and target appearance changes shall be carefully considered. Focus on applications of our amphibious spherical robots, an adaptive visual tracking algorithm was proposed on the basis of compressive tracking. A feature selection method was designed to choose random Haar-like feature templates in various scales by calculating Fisher's criterion functions of features. On this basis, a random feature pool, which tried to preserve discriminative features at different frames, were constructed and then maintained on-line to provide candidate appearance model of the target. Moreover, an adaptive update mechanism was adopted for selectively updating feature templates and classifier parameters of the improved compressive tracking algorithm, which alleviated the drift problem. Experimental results with various image sequences demonstrated the effectiveness and robustness of the proposed tracking algorithm, which can meet practical application requirements of the amphibious spherical robots.
KW - Adaptive Update
KW - Amphibious Spherical Robot
KW - Compressive Tracking
KW - Haar-like Feature
KW - Random Feature Pool
UR - http://www.scopus.com/inward/record.url?scp=84991258512&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2016.7558632
DO - 10.1109/ICMA.2016.7558632
M3 - Conference contribution
AN - SCOPUS:84991258512
T3 - 2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016
SP - 605
EP - 611
BT - 2016 IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2016
Y2 - 7 August 2016 through 10 August 2016
ER -