TY - GEN
T1 - On mining moving patterns for object tracking sensor networks
AU - Peng, Wen-Chih
AU - Ko, Yu Zen
AU - Lee, Wang Chien
PY - 2006/11/21
Y1 - 2006/11/21
N2 - In this paper, we propose a heterogeneous tracking model, referred to as HTM, to efficiently mine object moving patterns and track objects. Specifically, we use a variable memory Markov model to exploit the dependencies among object movements. Furthermore, due to the hierarchical nature of HTM, multi-resolution object moving patterns are provided. The proposed HTM is able to accurately predict the movements of objects and thus reduces the energy consumption for object tracking. Simulation results show that HTM not only is able to effectively mine object moving patterns but also save energy in tracking objects.
AB - In this paper, we propose a heterogeneous tracking model, referred to as HTM, to efficiently mine object moving patterns and track objects. Specifically, we use a variable memory Markov model to exploit the dependencies among object movements. Furthermore, due to the hierarchical nature of HTM, multi-resolution object moving patterns are provided. The proposed HTM is able to accurately predict the movements of objects and thus reduces the energy consumption for object tracking. Simulation results show that HTM not only is able to effectively mine object moving patterns but also save energy in tracking objects.
UR - http://www.scopus.com/inward/record.url?scp=33751054995&partnerID=8YFLogxK
U2 - 10.1109/MDM.2006.114
DO - 10.1109/MDM.2006.114
M3 - Conference contribution
AN - SCOPUS:33751054995
SN - 0769525261
SN - 9780769525266
T3 - Proceedings - IEEE International Conference on Mobile Data Management
BT - 7th International Conference on Mobile Data Management, 2006. MDM 2006
Y2 - 10 May 2006 through 12 May 2006
ER -