TY - GEN
T1 - Efficient visual servoing with the ABCshift tracking algorithm
AU - Stolkin, Rustam
AU - Florescu, Ionut
AU - Baron, Morgan
AU - Harrier, Colin
AU - Kocherov, Boris
PY - 2008
Y1 - 2008
N2 - Visual tracking algorithms have important robotic applications such as mobile robot guidance and servoed wide area surveillance systems. These applications ideally require vision algorithms which are robust to camera motion and scene change but are cheap and fast enough to run on small, low power embedded systems. Unfortunately most robust visual tracking algorithms are either computationally expensive or are restricted to a stationary camera. This paper describes a new color based tracking algorithm, the Adaptive Background CAMSHIFT (ABCshift) tracker and an associated technique, mean shift servoing, for efficient pan-tilt servoing of a motorized camera platform. ABCshift achieves robustness against camera motion and other scene changes by continuously relearning its background model at every frame. This also enables robustness in difficult scenes where the tracked object moves past backgrounds with which it shares significant colors. Despite this continuous machine learning, ABCshift needs minimal training and is remarkably computationally cheap. We first demonstrate how ABCshift tracks robustly in situations where related algorithms fail, and then show how it can be used for real time tracking with pan-tilt servo control using only a small embedded microcontroller.
AB - Visual tracking algorithms have important robotic applications such as mobile robot guidance and servoed wide area surveillance systems. These applications ideally require vision algorithms which are robust to camera motion and scene change but are cheap and fast enough to run on small, low power embedded systems. Unfortunately most robust visual tracking algorithms are either computationally expensive or are restricted to a stationary camera. This paper describes a new color based tracking algorithm, the Adaptive Background CAMSHIFT (ABCshift) tracker and an associated technique, mean shift servoing, for efficient pan-tilt servoing of a motorized camera platform. ABCshift achieves robustness against camera motion and other scene changes by continuously relearning its background model at every frame. This also enables robustness in difficult scenes where the tracked object moves past backgrounds with which it shares significant colors. Despite this continuous machine learning, ABCshift needs minimal training and is remarkably computationally cheap. We first demonstrate how ABCshift tracks robustly in situations where related algorithms fail, and then show how it can be used for real time tracking with pan-tilt servo control using only a small embedded microcontroller.
KW - ABCshift
KW - Adaptive background model
KW - CAMSHIFT
KW - Meanshift
KW - Servoing
KW - Tracking
UR - http://www.scopus.com/inward/record.url?scp=51649131001&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51649131001&partnerID=8YFLogxK
U2 - 10.1109/ROBOT.2008.4543701
DO - 10.1109/ROBOT.2008.4543701
M3 - Conference contribution
AN - SCOPUS:51649131001
SN - 9781424416479
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3219
EP - 3224
BT - 2008 IEEE International Conference on Robotics and Automation, ICRA 2008
T2 - 2008 IEEE International Conference on Robotics and Automation, ICRA 2008
Y2 - 19 May 2008 through 23 May 2008
ER -