Crowd simulation based on constrained and controlled group formation
| LEADER | caa a22 4500 | ||
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| 001 | 605540209 | ||
| 003 | CHVBK | ||
| 005 | 20210128100910.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00371-013-0900-7 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00371-013-0900-7 | ||
| 245 | 0 | 0 | |a Crowd simulation based on constrained and controlled group formation |h [Elektronische Daten] |c [Peng Zhang, Hong Liu, Yan-hui Ding] |
| 520 | 3 | |a Freestyle formations appear widely in animation of groups. Most existing algorithms for generating special formations focus on the visualization performances of target formations, while social dynamics factors in the process of crowd motion are ignored. Thus, disregarding those factors will decrease the bionic features and fidelity of the crowd motion. According to this problem, a method based on bionic intelligence algorithm and self-adaptive evaluation to generate special formations is proposed in this paper. Simulation effect with good fluency and lively interaction is generated by means of user interaction, data analysis and crowd motion. In this method, 3D reconstruction is used to repaint characters, graphics or patterns in the 3D modeling system to build the basic virtual scene. Then, station points are generated through interlacing cross sampling. Based on the concentric circles model of fitness, each individual, self-adaptively, chooses a target station point which matches it aptly. Finally, the Artificial Bee Colony algorithm is used for path planing to generate the optimum route to the destination without collision. Visual simulation experiments are also made on the platforms of ACIS/HOOPS and Maya. The results show that this method can generate the optimum target formation with natural motion features and in accordance with users' input. This method is also insensitive to the scale of crowd, exhibiting good performance when the number of individuals is large. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2013 | ||
| 690 | 7 | |a Crowd motion |2 nationallicence | |
| 690 | 7 | |a Freestyle formations |2 nationallicence | |
| 690 | 7 | |a Artificial bee colony algorithm |2 nationallicence | |
| 690 | 7 | |a 3D simulation |2 nationallicence | |
| 700 | 1 | |a Zhang |D Peng |u School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China |4 aut | |
| 700 | 1 | |a Liu |D Hong |u School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China |4 aut | |
| 700 | 1 | |a Ding |D Yan-hui |u School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China |4 aut | |
| 773 | 0 | |t The Visual Computer |d Springer Berlin Heidelberg |g 31/1(2015-01-01), 5-18 |x 0178-2789 |q 31:1<5 |1 2015 |2 31 |o 371 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00371-013-0900-7 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00371-013-0900-7 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zhang |D Peng |u School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Liu |D Hong |u School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ding |D Yan-hui |u School of Information Science and Engineering, Shandong Normal University, 250014, Jinan, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t The Visual Computer |d Springer Berlin Heidelberg |g 31/1(2015-01-01), 5-18 |x 0178-2789 |q 31:1<5 |1 2015 |2 31 |o 371 | ||