Person anomaly detection-based videos surveillance system in urban integrated pipe gallery

References

• Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Belopolsky, A., & Bengio, Y. (2016). Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688.
   Google Scholar
• Alvar, M., Torsello, A., Sanchez-Miralles, A., & Armingol, J. M. (2014). Abnormal behavior detection using dominant sets. Machine Vision and Applications, 25(5), 1351–1368. https://doi.org/10.1007/s00138-014-0615-4
   Web of Science ®Google Scholar
• Ashoka, K., & Shanmugathas, S. (2018). Factors influencing the mark-up decisions of infrastructure projects in developing countries: The case of Sri Lanka. Journal of System and Management Sciences, 8(2), 1–25. https://doi.org/10.1080/15623599.2019.1583849
   Google Scholar
• Bertini, M., Del Bimbo, A., & Seidenari, L. (2012). Multi-scale and real-time non-parametric approach for anomaly detection and localization. Computer Vision and Image Understanding, 116(3), 320–329. https://doi.org/10.1016/j.cviu.2011.09.009
   Web of Science ®Google Scholar
• Bogus, S. M., Molenaar, K. R., & Diekmann, J. E. (2005). Concurrent engineering approach to reducing design delivery time. Journal of Construction Engineering and Management, 131(11), 1179–1185. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:11(1179)
   Web of Science ®Google Scholar
• Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 15. https://doi.org/10.1145/1541880.1541882
   Web of Science ®Google Scholar
• Cheplygina, V., Tax, D. M., & Loog, M. (2015). Multiple instance learning with bag dissimilarities. Pattern Recognition, 48(1), 264–275. https://doi.org/10.1016/j.patcog.2014.07.022
   Web of Science ®Google Scholar
• Cui, X., Liu, Q., Gao, M., & Metaxas, D. N. (2011, June). Abnormal detection using interaction energy potentials. In CVPR 2011 (pp. 3161–3167). IEEE.
   Google Scholar
• Dai, Y., Zhu, X., Zhou, H., Mao, Z., & Wu, W. (2018). Trajectory tracking control for seafloor tracked vehicle by adaptive Neural-Fuzzy inference system algorithm. International Journal of Computers, Communications & Control, 13(4). https://doi.org/10.15837/ijccc.2018.4.3267
   Web of Science ®Google Scholar
• Datta, A., Shah, M., & Lobo, N. D. V. (2002, August). Person-on-person violence detection in video data. In Object recognition supported by user interaction for service robots (Vol. 1) (pp. 433–438). IEEE.
   Google Scholar
• Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997). Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1–2), 31–71. https://doi.org/10.1016/S0004-3702(96)00034-3
   Web of Science ®Google Scholar
• Ding, Y., Li, X., & Chen, F. (2019, March 1–2). Discussion on the design of integrated node of urban integrated pipe gallery. In IOP Conference Series: Earth and Environmental Science (Vol. 242, No. 6, p. 062085). IOP Publishing. https://doi.org/10.1088/1755-1315/242/6/062085
   Google Scholar
• Du, J., Li, Q., & Qiao, F. (2018). Impact of different ramp metering strategies on vehicle emissions along freeway segments. Journal of Transport & Health, 9, S51. https://doi.org/10.1016/j.jth.2018.05.036
   Web of Science ®Google Scholar
• Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., & Tian, Q. (2019, October 27–November 3). Centernet: Keypoint triplets for object detection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 6569–6578).
   Google Scholar
• Eigen, D., Puhrsch, C., & Fergus, R. (2014, December 8–13). Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems (pp. 2366–2374).
   Google Scholar
• Feng, Y., Yuan, Y., & Lu, X. (2017). Learning deep event models for crowd anomaly detection. Neurocomputing, 219, 548–556. doi:10.1016/j.neucom.2016.09.063
   Web of Science ®Google Scholar
• Gao, Y., Liu, H., Sun, X., Wang, C., & Liu, Y. (2016). Violence detection using oriented violent flows. Image and Vision Computing, 48, 37–41. https://doi.org/10.1016/j.imavis.2016.01.006
   Web of Science ®Google Scholar
• Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. https://doi.org/10.1148/radiology.143.1.7063747 doi: 10.1148/radiology.143.1.7063747
   PubMed Web of Science ®Google Scholar
• Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A. K., & Davis, L. S. (2016, June 27–30). Learning temporal regularity in video sequences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 733–742).
   Google Scholar
• He, J., Hu, J., Tang, Q., & Guo, S. (2010). Layout optimization of urban underground pipeline based on 3D digital city. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(Part II), 279–283.
   Google Scholar
• Hoque, I., Ahmed, S., & Sobuz, H. R. (2017). Identification of factors influencing accidents on construction sites. Journal of System and Management Sciences, 7(4), 1–16.
   Google Scholar
• Ilse, M., Tomczak, J. M., & Welling, M. (2018). Attention-based deep multiple instance learning. arXiv preprint arXiv:1802.04712.
   Google Scholar
• Kim, H., Lee, S., Kim, Y., Lee, S., Lee, D., Ju, J., & Myung, H. (2016). Weighted joint-based human behavior recognition algorithm using only depth information for low-cost intelligent video-surveillance system. Expert Systems with Applications, 45, 131–141. https://doi.org/10.1016/j.eswa.2015.09.035
   Web of Science ®Google Scholar
• Kooij, J. F., Liem, M. C., Krijnders, J. D., Andringa, T. C., & Gavrila, D. M. (2016). Multi-modal human aggression detection. Computer Vision and Image Understanding, 144, 106–120. https://doi.org/10.1016/j.cviu.2015.06.009
   Web of Science ®Google Scholar
• Law, H., & Deng, J. (2018, September 8–14). Cornernet: Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 734–750).
   Google Scholar
• Legrand, L., Blanpain, O., & Buyle-Bodin, F. (2004). Promoting the urban utilities tunnel technique using a decision-making approach. Tunnelling and Underground Space Technology, 19(1), 79–83. https://doi.org/10.1016/S0886-7798(03)00041-5
   Web of Science ®Google Scholar
• Levchuk, G., Pattipati, K., Fouse, A., McCormack, R., & Serfaty, D. (2018, March 26–28). Active inference in multi-agent systems: Context-driven collaboration and decentralized purpose-driven team adaptation. In 2018 AAAI Spring Symposium Series.
   Google Scholar
• Li, Q., Xu, G., & Wei, H. (2018). Research on the operations management system of utility tunnel based on BIM. Chinese Journal of Underground Space and Engineering, 14(2), 287–292.
   Google Scholar
• Li, W., Mahadevan, V., & Vasconcelos, N. (2013). Anomaly detection and localization in crowded scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1), 18–32. https://doi.org/10.1109/TPAMI.2013.111
   Web of Science ®Google Scholar
• Liu, G., Wu, J., & Zhou, Z. H. (2012, November 14–16). Key instance detection in multi-instance learning. In Asian Conference on Machine Learning (pp. 253–268).
   Google Scholar
• Liu, W., Luo, W., Lian, D., & Gao, S. (2018, June 18–23). Future frame prediction for anomaly detection–a new baseline. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6536–6545).
   Google Scholar
• Lu, C., Shi, J., & Jia, J. (2013, December 1–8). Abnormal event detection at 150 fps in matlab. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2720–2727).
   Google Scholar
• Mabrouk, A. B., & Zagrouba, E. (2017). Spatio-temporal feature using optical flow based distribution for violence detection. Pattern Recognition Letters, 92, 62–67. https://doi.org/10.1016/j.patrec.2017.04.015
   Web of Science ®Google Scholar
• Mabrouk, A. B., & Zagrouba, E. (2018). Abnormal behavior recognition for intelligent video surveillance systems: A review. Expert Systems with Applications, 91, 480–491. https://doi.org/10.1016/j.eswa.2017.09.029
   Web of Science ®Google Scholar
• Masci, J., Meier, U., Cireşan, D., & Schmidhuber, J. (2011, June 14–17). Stacked convolutional auto-encoders for hierarchical feature extraction. In International Conference on Artificial Neural Networks (pp. 52–59). Springer, Berlin, Heidelberg.
   Google Scholar
• Mohammadi, S., Perina, A., Kiani, H., & Murino, V. (2016, October 8–16). Angry crowds: Detecting violent events in videos. In European Conference on Computer Vision (pp. 3–18). Springer, Cham.
   Google Scholar
• Newell, A., Yang, K., & Deng, J. (2016, October 8–16). Stacked hourglass networks for human pose estimation. In European Conference on Computer Vision (pp. 483–499). Springer, Cham.
   Google Scholar
• Norton, M., & Uryasev, S. (2019). Maximization of auc and buffered auc in binary classification. Mathematical Programming, 174(1-2), 575–612. https://doi.org/10.1007/s10107-018-1312-2
   Web of Science ®Google Scholar
• Provost, F. J., & Fawcett, T. (1997, August). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In KDD (Vol. 97, pp. 43–48).
   Google Scholar
• Quellec, G., Cazuguel, G., Cochener, B., & Lamard, M. (2017). Multiple-instance learning for medical image and video analysis. IEEE Reviews in Biomedical Engineering, 10, 213–234. https://doi.org/10.1109/RBME.2017.2651164
   PubMedGoogle Scholar
• Raykar, V. C., Krishnapuram, B., Bi, J., Dundar, M., & Rao, R. B. (2008, July 5–9). Bayesian multiple instance learning: Automatic feature selection and inductive transfer. In ICML (Vol. 8) (pp. 808–815).
   Google Scholar
• Ren, K., Yang, H., Zhao, Y., Chen, W., Xue, M., Miao, H., Huang, S., & Liu, J. (2018). A robust AUC maximization framework with simultaneous outlier detection and feature selection for positive-unlabeled classification. IEEE Transactions on Neural Networks and Learning Systems, 30(10), 3072–3083. https://doi.org/10.1109/TNNLS.2018.2870666
   PubMed Web of Science ®Google Scholar
• Ren, W., Li, G., Sun, B., & Huang, K. (2015). Unsupervised kernel learning for abnormal events detection. The Visual Computer, 31(3), 245–255. https://doi.org/10.1007/s00371-013-0915-0
   Web of Science ®Google Scholar
• Sakai, T., Niu, G., & Sugiyama, M. (2018). Semi-supervised AUC optimization based on positive-unlabeled learning. Machine Learning, 107(4), 767–794. https://doi.org/10.1007/s10994-017-5678-9
   Web of Science ®Google Scholar
• Snellman, L. (2016). Social Entrepreneurship: Making change in the world. Journal of Logistics, Informatics and Service Science, 3(1), 1–25.
   Google Scholar
• Sultani, W., Chen, C., & Shah, M. (2018, June 18–23). Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6479–6488).
   Google Scholar
• Sun, Y., Zheng, L., Deng, W., & Wang, S. (2017, October 22–29). Svdnet for pedestrian retrieval. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3800–3808).
   Google Scholar
• Tabsh, Y., & Davidavičienė, V. (2019). ICT in energy security and efficiency assurance. Journal of System and Management Sciences, 9(1), 1–18.
   Google Scholar
• Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015, December 11–18). Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4489–4497). https://doi.org/10.1109/ICCV.2015.510
   Google Scholar
• Wei, Y., Ma, J., Dang, B., Luo, D., Dang, N., Li, J., & Li, Y. (2018, September 14–15). Linkage function of monitoring and alarm system for urban utility tunnel engineering. In 2018 7th International Conference on Energy and Environmental Protection (ICEEP 2018). Atlantis Press. https://doi.org/10.2991/iceep-18.2018.170
   Google Scholar
• Xiang, T., & Gong, S. (2008). Video behavior profiling for anomaly detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(5), 893–908. https://doi.org/10.1109/TPAMI.2007.70731
   PubMed Web of Science ®Google Scholar
• Xu, W., & Yin, Y. (2018). Functional objectives decisionmaking of discrete manufacturing system based on integrated ant colony optimization and particle swarm optimization approach. Advances in Production Engineering & Management, 13(4), 389. https://doi.org/10.14743/apem2018.4.298
   Web of Science ®Google Scholar
• Zhao, P. X., Gao, W. Q., Han, X., & Luo, W. H. (2019). Bi-objective collaborative scheduling optimization of Airport Ferry vehicle and tractor. International Journal of Simulation Modelling (IJSIMM), 18(2), 355–365. https://doi.org/10.2507/DSIMM18(2)CO9 doi: 10.2507/IJSIMM18(2)CO9
   Web of Science ®Google Scholar
• Zhou, X., Wang, D., & Krähenbühl, P. (2019). Objects as points. arXiv preprint arXiv:1904.07850.
   Google Scholar
• Zhuang, X., Chen, S., Wu, J., & Wang, C. (2018, September 14–16). Underground comprehensive pipe gallery illegal intrusion wireless Sensing system. In 2018 IEEE International Conference on signal processing, Communications and Computing (ICSPCC) (pp. 1–5). https://doi.org/10.1109/ICSPCC.2018.8567732
   Google Scholar