TY - GEN
T1 - GPCD
T2 - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006
AU - Yu, Zhiwen
AU - Wong, Hau-San
PY - 2006
Y1 - 2006
N2 - Given a time horizon parameter h and an object set O, predictive collision detection finds all the object pairs 〈 oi, oj, t i 〉 which will collide in the future time interval [t, t + h] (where 1 ≤ i, j ≤ n, ti ∈ [t, t + h]). Although there are a number of state-of-the-art approaches to solve collision detection problems, predictive collision detection is addressed for the first time. In this paper, we propose a grid-based predictive collision detection algorithm (GPCD), which is a general technique for the efficient detection of the collision of object pairs in a future time interval. GPCD first determines a candidate list which stores the object pairs having a non-zero probability to collide in a future time. Then, GPCD achieves low running time based on two pruning strategies: (i) space intersection test and (ii) time intersection test. These two pruning strategies eliminate most of the false collision cases in an initial filtering phase. In the refinement phase, a bounding-volume tree is applied to refine the detection results. Our experiments show that GPCD works well for the purpose of predictive collision detection. © 2006 IEEE.
AB - Given a time horizon parameter h and an object set O, predictive collision detection finds all the object pairs 〈 oi, oj, t i 〉 which will collide in the future time interval [t, t + h] (where 1 ≤ i, j ≤ n, ti ∈ [t, t + h]). Although there are a number of state-of-the-art approaches to solve collision detection problems, predictive collision detection is addressed for the first time. In this paper, we propose a grid-based predictive collision detection algorithm (GPCD), which is a general technique for the efficient detection of the collision of object pairs in a future time interval. GPCD first determines a candidate list which stores the object pairs having a non-zero probability to collide in a future time. Then, GPCD achieves low running time based on two pruning strategies: (i) space intersection test and (ii) time intersection test. These two pruning strategies eliminate most of the false collision cases in an initial filtering phase. In the refinement phase, a bounding-volume tree is applied to refine the detection results. Our experiments show that GPCD works well for the purpose of predictive collision detection. © 2006 IEEE.
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-34247621385&origin=recordpage
U2 - 10.1109/ICME.2006.262708
DO - 10.1109/ICME.2006.262708
M3 - 32_Refereed conference paper (with ISBN/ISSN)
SN - 1424403677
SN - 9781424403677
VL - 2006
SP - 1025
EP - 1028
BT - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
Y2 - 9 July 2006 through 12 July 2006
ER -