TY - GEN
T1 - Fast task-specific target detection via graph based constraints representation and checking
AU - Luan, Wentao
AU - Yang, Yezhou
AU - Fermuller, Cornelia
AU - Baras, John S.
N1 - Funding Information:
This work was funded by the support of DARPA (through ARO) grant W911NF1410384, by NSF through grants CNS-1544787 and SMA-1540917, and the University research program of Northrop Grumman.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - We present a framework for fast target detection in real-world robotics applications. Considering that an intelligent agent attends to a task-specific object target during execution, our goal is to detect the object efficiently. We propose the concept of early recognition, which influences the candidate proposal process to achieve fast and reliable detection performance. To check the target constraints efficiently, we put forward a novel policy which generates a sub-optimal checking order, and we prove that it has bounded time cost compared to the optimal checking sequence, which is not achievable in polynomial time. Experiments on two different scenarios: 1) rigid object and 2) non-rigid body part detection validate our pipeline. To show that our method is widely applicable, we further present a human-robot interaction system based on our non-rigid body part detection.
AB - We present a framework for fast target detection in real-world robotics applications. Considering that an intelligent agent attends to a task-specific object target during execution, our goal is to detect the object efficiently. We propose the concept of early recognition, which influences the candidate proposal process to achieve fast and reliable detection performance. To check the target constraints efficiently, we put forward a novel policy which generates a sub-optimal checking order, and we prove that it has bounded time cost compared to the optimal checking sequence, which is not achievable in polynomial time. Experiments on two different scenarios: 1) rigid object and 2) non-rigid body part detection validate our pipeline. To show that our method is widely applicable, we further present a human-robot interaction system based on our non-rigid body part detection.
UR - http://www.scopus.com/inward/record.url?scp=85027996060&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027996060&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2017.7989458
DO - 10.1109/ICRA.2017.7989458
M3 - Conference contribution
AN - SCOPUS:85027996060
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3984
EP - 3991
BT - ICRA 2017 - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Y2 - 29 May 2017 through 3 June 2017
ER -