Abstract
Today video surveillance systems are omnipresent and increasingly complex, requiring the simultaneous recording, storage, and transfer of information in real time. Such requirements necessitate the efficient storage of large quantities of data and corresponding techniques to optimize the flow of pertinent information. We propose a novel algorithm for the conversion of video data into a simple vector format which is then used to reconstruct a video of the given scene including all needed actionable information for both security personnel and automation. Neural networks are used to detect and localize human keypoints (corresponding to human joints), which can then be stored to render the scene depicting the actions of individuals and more complex events relevant to security concerns.
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References
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, 14–16 April 2014, Conference Track Proceedings (2014)
Güler, R.A., Neverova, N., Kokkinos, I.: DensePose: dense human pose estimation in the wild. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 7297–7306 (2018). https://doi.org/10.1109/CVPR.2018.00762
Dong, H., Liang, X., Gong, K., Lai, H., Zhu, J., Yin, J.: Soft-gated warping-GAN for pose-guided person image synthesis. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS 2018), pp. 472–482. Curran Associates Inc., Red Hook (2018)
Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S.-E., Sheikh, Y.A.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172–186 (2019). https://doi.org/10.1109/TPAMI.2019.2929257
Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019). https://github.com/facebookresearch/detectron2
Harris, C.R., Millman, K.J., van der Walt, S.J., et al.: Array programming with NumPy. Nature 585, 357–362 (2020). https://doi.org/10.1038/s41586-020-2649-2
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Mackiewicz, A., Ratajczak, W.: Principal components analysis (PCA). Comput. Geosci. 19(3), 303–342 (1993)
Acknowledgments
The reported study was funded by Russian Foundation of Basic Researches, project number 19-29-09090.
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Zuev, I., Gilya-Zetinov, A., Khelvas, A., Konyagin, E., Segre, J. (2022). Humans Digital Avatar Reconstruction for Tactical Situations Animation. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_40
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DOI: https://doi.org/10.1007/978-3-030-80119-9_40
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