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Research on role modeling and behavior control of virtual reality animation interactive system in Internet of Things

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Abstract

To solve the problems of poor real-time collision accuracy and low efficiency of modeling in the virtual reality environment, we propounded a depth image-based 3D modeling system and a hybrid intelligent collision detection algorithm. With the development of the 3D animation interactive system as an example, this paper uses 3D modeling technology based on depth images to build single role models, then reorganizes and merges the models to form 3D scenes. The hybrid intelligent collision detection algorithm, which combines the quantum behavior particle swarm optimization algorithm and the differential algorithm, improves the collision detection efficiency and accuracy and realizes behavior control of the characters in the interactive system. The experimental result shows that the 3D modeling technology based on depth images has greatly improved the accuracy and quantity of model texture and motion rate. By comparing the hybrid intelligent collision detection algorithm, the QPSO algorithm, and the FDH bounding box for collision detection, we conclude that the algorithm used in this paper has a shorter average collision time, more stable role behavior control, and better robustness.

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References

  1. Ho, J.C.F.: Real-world and virtual-world practices for virtual reality games: effects on spatial perception and game performance. Multimodal Technol. Interact. 4(1), 1–15 (2020). https://doi.org/10.3390/mti4010001

    Article  Google Scholar 

  2. Noureddine, E.: Augmented reality and virtual reality in education. Myth or reality? Int. J. Emerg. Technol. Learn. (IJET) 14(3), 234–242 (2019). https://doi.org/10.3991/ijet.v14i03.9289

    Article  Google Scholar 

  3. Parker, E., Saker, M.: Art museums and the incorporation of virtual reality: examining the impact of VR on spatial and social norms. Converg. Int. J. Res. New Med. Technol. (2020). https://doi.org/10.1177/1354856519897251

    Article  Google Scholar 

  4. Llyrap, C., Headleand, C.J.: Movement modalities in virtual reality: a case study from ocean rift examining the best practices in accessibility, comfort, and immersion. IEEE Consum. Electron. Mag. 8(1), 30–35 (2019). https://doi.org/10.1109/MCE.2018.2867971

    Article  Google Scholar 

  5. Zhang, K.: Research on immersive 3D virtual roaming technology. J. Changchun Univ. Sci. Technol. 8(11), 104–106 (2016)

    Google Scholar 

  6. Hai, Y.D., Hironori, M., Shoichi, H.: Continuous collision detection for virtual proxy haptic rendering of deformable triangular mesh models. IEEE Trans. Haptics 12(4), 624–634 (2019). https://doi.org/10.1109/TOH.2019.2934104

    Article  Google Scholar 

  7. Dingle, B.M., Eubanks, J.C., Janasak, K.: 3D RAM modeling and simulation in a model based systems engineering environment. In: 2020 Annual Reliability and Maintainability Symposium (RAMS), pp. 1–6 (2020). https://doi.org/10.1109/RAMS48030.2020.9153644

  8. Zhang, Z.W., Yang, Z.P., Ma, C.Y., Luo, L.J.: Deep generative modeling for scene synthesis via hybrid representations. ACM Trans. 39(2), 17–21 (2020). https://doi.org/10.1145/3381866

    Article  Google Scholar 

  9. Chen, S., et al.: 3D object reconstruction with Kinect based on QR code calibration. In: 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), pp. 459–463 (2020). https://doi.org/10.1109/ICAICA50127.2020.9181884

  10. Melero, F.J.: Fast collision detection between high-resolution polygonal models. Comput. Graph. 83(10), 97–106 (2019). https://doi.org/10.1016/j.cag.2019.07.006

    Article  Google Scholar 

  11. Xia, Y., Leung, H.: Performance analysis of statistical optimal data fusion algorithms. Inf. Sci. 277, 808–824 (2014). https://doi.org/10.1016/j.ins.2014.03.015

    Article  MathSciNet  MATH  Google Scholar 

  12. Yang, L.H., Wang, Y.M., Su, Q., Fu, Y.G.: Multi-attribute search framework for optimizing extended belief rule-based systems. Inf. Sci. 370, 159–183 (2016). https://doi.org/10.1016/j.ins.2016.07.067

    Article  Google Scholar 

  13. Wang, H., Zhang, X., Zhou, L., Lu, X., Wang, C.: Intersection detection algorithm based on hybrid bounding box for geological modeling with faults. IEEE Access 8, 29538–29546 (2020). https://doi.org/10.1109/ACCESS.2020.2972317

    Article  Google Scholar 

  14. Chitalu, F.M., Dubach, C., Komura, T.: Binary ostensibly-implicit trees for fast collision detection. Comput. Graph. Forum 39(2), 509–521 (2020). https://doi.org/10.1111/cgf.13948

    Article  Google Scholar 

  15. Lu, R., Hu, H.D.: An improved artificial bee colony algorithm with fast strategy and its application. Comput. Electr. Eng. 78(9), 79–88 (2019). https://doi.org/10.1016/j.compeleceng.2019.06.021

    Article  Google Scholar 

  16. Chen, C.M., Xiang, B., Liu, Y., Wang, K.H.: A secure authentication protocol for internet of vehicles. Access 7(1), 12047–12057 (2019). https://doi.org/10.1109/ACCESS.2019.2891105

    Article  Google Scholar 

  17. Chen, C.M., Wang, K.H., Yeh, K.H., Xiang, K.B., Wu, T.Y.: Attacks and solutions on a three-party password-based authenticated key exchange protocol for wireless communications. J. Ambient Intell. Humaniz. Comput. 8(10), 3133–3142 (2019). https://doi.org/10.1007/s12652-018-1029-3

    Article  Google Scholar 

  18. Cheng, H.J., Su, Z.H., Xiong, N.X., Xiao, Y.: Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model. Inf. Sci. 329, 461–477 (2016). https://doi.org/10.1016/j.ins.2015.09.039

    Article  MATH  Google Scholar 

  19. Wang, J., Zhang, X.M., Lin, Y.F., Ge, X.H., Han, Q.L.: Event-triggered dissipative control for networked stochastic systems under non-uniform sampling. Inf. Sci. 447, 216–228 (2018). https://doi.org/10.1016/j.ins.2018.03.003

    Article  MATH  Google Scholar 

  20. Yu, Y.L., Sun, Z.Z.: Sparse coding extreme learning machine for classification. Neurocomputing 261, 50–56 (2017). https://doi.org/10.1016/j.neucom.2016.06.078

    Article  Google Scholar 

  21. Cheng, T., Jiang, H., Wang, F., Hua, Y., Feng, D., Guo, W., et al.: Using high-bandwidth networks efficiently for fast graph computation. IEEE Trans. Parallel Distrib. Syst. 30(5), 1170–1183 (2019). https://doi.org/10.1109/TPDS.71

    Article  Google Scholar 

  22. Guo, W., Liu, G., et al.: A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning. Front. Comput. Sci. 8(2), 203–216 (2014). https://doi.org/10.1007/s11704-014-3008-y

    Article  MathSciNet  Google Scholar 

  23. Guo, W., Chen, G.: Human action recognition via multi-task learning base on spatial-temporal feature. Inf. Sci. 320(1), 418–428 (2015). https://doi.org/10.1016/j.ins.2015.04.034

    Article  MathSciNet  Google Scholar 

  24. Guo, W., Lin, B., et al.: Cost-driven scheduling for deadline-based workflow in multiloads. IEEE Trans. Netw. Serv. Manag. 15(4), 1571–1585 (2018). https://doi.org/10.1109/TNSM.2018.2872066

    Article  Google Scholar 

  25. Guo, W., Li, J., et al.: A PSO-optimized real-time fault-tolerant task allocation algorithm in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 26(12), 3236–3249 (2015). https://doi.org/10.1109/TPDS.2014.2386343

    Article  Google Scholar 

  26. Wang, S., Guo, W.: Sparse multi-graph embedding for multimodal feature representation. IEEE Trans. Multimed. 19(7), 1454–1466 (2017a). https://doi.org/10.1109/TMM.2017.2663324

    Article  Google Scholar 

  27. Wang, S.P., Guo, W.Z.: Robust co-clustering via dual local learning and high-order matrix factorization. Knowl. Based Syst. 138, 176–187 (2017b). https://doi.org/10.1016/j.knosys.2017.09.033

    Article  Google Scholar 

  28. Niu, Y., Lin, W., Ke, X., et al.: Fitting-based optimisation for image visual salient object detection. IET Comput. Vis. 11(2), 161–172 (2017). https://doi.org/10.1049/iet-cvi.2016.0027

    Article  Google Scholar 

  29. Chen, F., Deng, P., Wan, H.F., et al.: Data mining for the internet of things: literature review and challenges. Int. J. Distrib. Sens. Netw. 11(8), 1–14 (2015). https://doi.org/10.1155/2015/431047

    Article  Google Scholar 

  30. Pan, J.S., Lee, C.Y., et al.: Novel systolization of subquadratic space complexity multipliers based on Toeplitz matrix–vector product approach. IEEE Trans. Very Large Scale Integr. Syst. 27(7), 1614–1622 (2019). https://doi.org/10.1109/TVLSI.2019.2903289

    Article  Google Scholar 

  31. Pan, J.S., Hu, P., Chu, S.C.: Novel parallel heterogeneous meta-heuristic and its communication strategies for the prediction of wind power. Processes 7(11), 845 (2019). https://doi.org/10.3390/pr7110845

    Article  Google Scholar 

  32. Guo, Y., Du, L., Chen, J.: Max-margin multi-scale convolutional factor analysis model with application to image classification. Expert Syst. Appl. 133(1), 21–33 (2019). https://doi.org/10.1016/j.eswa.2019.04.012

    Article  Google Scholar 

  33. Liu, D., Shi, G.: Ship collision risk assessment based on collision detection algorithm. IEEE Access. 8, 161969–161980 (2020). https://doi.org/10.1109/ACCESS.2020.3013957

    Article  Google Scholar 

  34. Salahshour, E., Malekzadeh, M.: Quantum neural network-based intelligent controller design for CSTR using modified particle swarm optimization algorithm. Trans. Inst. Meas. Control 41(2), 1–13 (2019). https://doi.org/10.1177/0142331218764566

    Article  Google Scholar 

  35. Ma, T.H., Liu, Q., Cao, J., Tian, Y., Al-Dhelaan, A., Al-Rodhaan, M.: LGIEM: global and local node influence based community detection. Future Gener. Comput. Syst. 105, 533–546 (2020). https://doi.org/10.1016/j.future.2019.12.022

    Article  Google Scholar 

  36. Ye, Q., Li, Z., Fu, L., Zhang, Z., Yang, W., Yang, G.: Nonpeaked discriminant analysis for data representation. IEEE Trans. Neural Netw. Learn. Syst. 30(12), 3818–3832 (2019). https://doi.org/10.1109/TNNLS.2019.2944869

    Article  MathSciNet  Google Scholar 

  37. Shen, Z., Patrick, P., Lee, C., Shu, J., Guo, W.: Encoding-aware data placement for efficient degraded reads in XOR-coded storage systems: algorithms and evaluation. IEEE Trans. Parallel Distrib. Syst. 29(12), 2757–2770 (2018). https://doi.org/10.1177/10.1109/TPDS.71

    Article  Google Scholar 

  38. Park, J.S., Manocha, D.: Efficient probabilistic collision detection for non-Gaussian noise distributions. IEEE Robot. Autom. Lett. 5(2), 1024–1031 (2020). https://doi.org/10.1109/LRA.2020.2966404

    Article  Google Scholar 

  39. Huang, X., Liu, G., Guo, W., et al.: Obstacle-avoiding algorithm in X-architecture based on discrete particle swarm optimization for VLSI design. ACM Trans. Des. Autom. Electron. Syst. 20(2), 1–28 (2015). https://doi.org/10.1145/2699862

    Article  Google Scholar 

  40. Huang, X., Guo, W., et al.: FH-OAOS: a fast 4-step heuristic for obstacle-avoiding octilinear architecture router construction. ACM Trans. Des. Autom. Electron. Syst. 21(3), 1–30 (2016). https://doi.org/10.1145/2856033

    Article  Google Scholar 

  41. Huang, X., Guo, W., Liu, G., Chen, G.: MLXR: multi-layer obstacle-avoiding X-architecture Steiner tree construction for VLSI routing. Sci. China Inf. Sci. 60(1), 1–3 (2017). https://doi.org/10.1007/s11432-015-0850-4

    Article  Google Scholar 

  42. Niu, Y., Chen, J.W., Guo, W.: Meta-metric for saliency detection evaluation metrics based on application preference. Multimed Tools Appl 77(20), 26351–26369 (2018). https://doi.org/10.1007/s11042-018-5863-2

    Article  Google Scholar 

  43. Guo, K., Guo, W., et al.: Community discovery by propagating local and global information based on the MapReduce Model. Inf. Sci. 323, 73–93 (2015). https://doi.org/10.1016/j.ins.2015.06.032

    Article  MathSciNet  Google Scholar 

  44. Das, N., Yip, M.: Learning-based proxy collision detection for robot motion planning applications. IEEE Trans. Robot. 36(4), 1096–1114 (2020). https://doi.org/10.1109/TRO.2020.2974094

    Article  Google Scholar 

  45. Yelghi, A., Köse, C.: A modified firefly algorithm for global minimum optimization. Appl. Soft Comput. 62, 29–44 (2018). https://doi.org/10.1016/j.asoc.2017.10.032

    Article  Google Scholar 

  46. Zhang, C., Wei, S.J.: Simulation design of group animation behavior control based on firefly algorithm. J. Chongqing Univ. Technol. 8(11), 101–106 (2017)

    Google Scholar 

  47. Zhong, S.P., Chen, T.S., He, F.Y., Niu, Y.Z.: Fast Gaussian kernel learning for classification tasks based on specially structured global optimization. Neural Netw. 57, 51–62 (2014). https://doi.org/10.1016/j.neunet.2014.05.014

    Article  MATH  Google Scholar 

  48. Zhang, S., Xia, Y., Wang, J.: A complex-valued projection neural network for constrained optimization of real functions in complex variables. IEEE Trans. Neural Netw. Learn. Syst. 26(12), 3227–3238 (2015). https://doi.org/10.1109/TNNLS.2015.2441697

    Article  MathSciNet  Google Scholar 

  49. Liu, G., Guo, W., et al.: A PSO-based-timing-driven Octilinear Steiner Tree algorithm for VLSI routing considering bend reduction. Soft. Comput. 19(5), 1153–1169 (2015a). https://doi.org/10.1007/s00500-014-1329-2

    Article  MATH  Google Scholar 

  50. Liu, G., Guo, W., et al.: XGRouter: high-quality global router in X-architecture with particle swarm optimization. Front. Comput. Sci. 9(4), 576–594 (2015b). https://doi.org/10.1007/s11704-015-4017-1

    Article  Google Scholar 

  51. Liu, G., Huang, X., Guo, W., Niu, Y., Chen, G.: Multilayer obstacle-avoiding X-architecture Steiner minimal tree construction based on particle swarm optimization. IEEE Trans. Cybern. 45(5), 989–1002 (2015). https://doi.org/10.1109/TCYB.2014.2342713

    Article  Google Scholar 

  52. Liu, G., Chen, Z., Zhuang, Z., Guo, W., et al.: A unified algorithm based on HTS and self-adapting PSO for the construction of octagonal and rectilinear SMT. Soft. Comput. 24(6), 3943–3961 (2020). https://doi.org/10.1007/s00500-019-04165-2

    Article  Google Scholar 

  53. Luo, F., Guo, W., et al.: A multi-label classification algorithm based on Kernel extreme learning machine. Neurocomputing 260, 313–320 (2016). https://doi.org/10.1016/j.neucom.2017.04.052

    Article  Google Scholar 

  54. Netto, M.A., Buyya, R.: Offer-based scheduling of deadline-constrained bag-of-tasks applications for utility computing systems. In: International Heterogeneity in Computing Workshop, in conjunction with the 23rd IEEE International Parallel and Distributed Processing Symposium, 2009, IEEE, pp. 1530–2075 (2009). https://doi.org/10.1109/IPDPS.2009.5160910

  55. Jin, Y.X., Wang, H.: Modeling method of multi-precision cloth simulation based on QPSO algorithm. Comput. Eng. Appl. 9(4), 154–160 (2019)

    Google Scholar 

  56. Wang, J., Wu, Q.Y.: Modeling indoor scenes with repetitions from 3D raw point data. Comput. Aided Des. 94, 1–15 (2018). https://doi.org/10.1016/j.cad.2017.09.001

    Article  Google Scholar 

  57. Krichenbauer, M., Yamamoto, G.: Evaluating the effect of positional head-tracking on task performance in 3D modeling user interfaces. Comput. Graph. 65, 22–30 (2017a). https://doi.org/10.1016/j.cag.2017.04.002

    Article  Google Scholar 

  58. Krichenbauer, M., Yamamoto, G.: Augmented reality vs virtual reality for 3D object manipulation. IEEE Trans. Vis. Comput. Graph. 24(2), 1038–1048 (2017b). https://doi.org/10.1109/TVCG.2017.2658570

    Article  Google Scholar 

  59. Jun, C., Lee, J.Y.: Automatized modeling of a human engineering simulation using Kinect. Robot. Comput. Integr. Manuf. 55, 1–5 (2018). https://doi.org/10.1016/j.rcim.2018.03.014

    Article  Google Scholar 

  60. Zhang, Y.M., Chen, C.W.: A Kinect-based approach for 3D pavement surface reconstruction and cracking recognition. IEEE Trans. Intell. Transp. Syst. 19(12), 1–12 (2018). https://doi.org/10.1109/TITS.2018.2791476

    Article  Google Scholar 

  61. Liu, S., Gong, G.H., Xiao, L.H.: Study of rapid face modeling technology based on Kinect. Int. J. Model. Simul. Sci. Comput. 9(01), 17500541–175005418 (2017). https://doi.org/10.1142/S1793962317500544

    Article  Google Scholar 

  62. Wang, C., Chan, S.C.: Superpixel-based color–depth restoration and dynamic environment modeling for Kinect-assisted image-based rendering systems. Vis. Comput. 34, 67–81 (2016). https://doi.org/10.1007/s00371-016-1312-2

    Article  Google Scholar 

  63. Huang, H.F., Chang, F.: Factors affecting usability of 3D model learning in a virtual reality environment. Interact. Learn. Environ. 11, 1–14 (2018). https://doi.org/10.1080/10494820.2019.1691605

    Article  Google Scholar 

  64. Sun, J.R., Lu, X.M.: Optimization algorithm of collision detection based on intersection of hybrid bounding box and triangle. Comput. Eng. Appl. 6(8), 198–203 (2018)

    Google Scholar 

  65. Ding, X.J.: Research on collision detection algorithm based on OBB. Appl. Mech. Mater. 433, 936–939 (2013). https://doi.org/10.4028/www.scientific.net/AMM.433-435.936

    Article  Google Scholar 

  66. Jung, W.C., Wang, W.P.: Efficient collision detection using a dual OBB-sphere bounding volume hierarchy. Comput. Aided Des. 42(1), 50–57 (2010). https://doi.org/10.1016/j.cad.2009.04.010

    Article  Google Scholar 

  67. Liao, Y.F., Qin, G.J.: Particle swarm optimization research base on quantum self-learning behaviour. J. Comput. Methods Sci. Eng. 20(1), 91–99 (2020). https://doi.org/10.3233/JCM-193644

    Article  MathSciNet  Google Scholar 

  68. Wang, S.H., Li, Y.Z.: Self-adaptive mutation differential evolution algorithm based on particle swarm optimization. Appl. Soft Comput. 81, 1–22 (2019). https://doi.org/10.1016/j.asoc.2019.105496

    Article  Google Scholar 

  69. Zhang, J.H., Dong, Z.: Parameter combination framework for the differential evolution algorithm. Algorithms 12(4), 1–22 (2019). https://doi.org/10.3390/a12040071

    Article  MathSciNet  MATH  Google Scholar 

  70. Dereventsov, A.V., Temlyakov, V.N.: A unified way of analyzing some greedy algorithms. J. Funct. Anal. 277(12), 1–30 (2019). https://doi.org/10.1016/j.jfa.2019.108286

    Article  MathSciNet  MATH  Google Scholar 

  71. Koh, W., Narain, R., O’Brien, J.F.: View-dependent adaptive cloth simulation with buckling compensation. IEEE Trans. Vis. Comput. Graph. 21(10), 1138–1145 (2015). https://doi.org/10.1109/tvcg.2015.2446482

    Article  Google Scholar 

  72. Lin, D.K., Huang, S.G.: Feature selection based on differential evolution and forest optimization. Minicomput. Syst. 8(3), 1210–1214 (2019)

    Google Scholar 

  73. Liu, W.J., Wang, J.G., Lv, D.Z.: Particle swarm optimization algorithm with operator perturbation. Mech. Des. Manuf. 12(5), 226–228 (2017)

    Google Scholar 

  74. Yuan, F.N., Xia, X., Shi, J.T.: Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns. Inf. Sci. 460, 202–222 (2018). https://doi.org/10.1016/j.ins.2018.05.033

    Article  Google Scholar 

  75. Wang, H.Y., Liu, S.G.: A collision detection algorithm using AABB and Octree space division. Adv. Mater. Res. 989, 2389–2392 (2014). https://doi.org/10.4028/www.scientific.net/AMR.989-994.2389

    Article  Google Scholar 

  76. Xue, T., Li, R.F.: Trajectory planning for autonomous mobile robot using a hybrid improved QPSO algorithm. Soft Comput. 21, 2421–2437 (2017). https://doi.org/10.1007/s00500-015-1956-2

    Article  Google Scholar 

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Acknowledgements

Research on Interactive Design of 3D Animation Based on Virtual Reality Technology (no. 2018GkQNCX042). Research on the mechanism of urban waste classification and recycling in the artificial intelligence environment (no. 2020GZGJ315). “MOOC+SPOC” Hybrid Teaching Model Oriented to Deep Learning (no. 19GGZ006). Research and implementation of online course knowledge recommendation system based on learning diagnosis model (no. 2020KTSCX378). Research on the third language teaching quality monitoring mechanism based on PDCA Cycle Theory (no. 2020WQNCX109). Key projects of social science and technology development in Dongguan (no. 2020507156156). Special fund for science and technology innovation strategy of Guangdong Province (no. pdjh2020a1261).

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Gan, B., Zhang, C., Chen, Y. et al. Research on role modeling and behavior control of virtual reality animation interactive system in Internet of Things. J Real-Time Image Proc 18, 1069–1083 (2021). https://doi.org/10.1007/s11554-020-01046-y

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