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Agee, P, Gao, X, Paige, F, McCoy, A and Kleiner, B (2021) A human-centred approach to smart housing. Building Research & Information, 49(01), 84–99.

Erişen, S (2021) Incremental transformation of spatial intelligence from smart systems to sensorial infrastructures. Building Research & Information, 49(01), 113–26.

Fettermann, D C, Borriello, A, Pellegrini, A, Cavalcante, C G, Rose, J M and Burke, P F (2021) Getting smarter about household energy: the who and what of demand for smart meters. Building Research & Information, 49(01), 100–12.

Fu, Y and Zhu, J (2021) Trusted data infrastructure for smart cities: a blockchain perspective. Building Research & Information, 49(01), 21–37.

Ihm, S, Lee, H, Lee, E and Park, Y (2021) A policy knowledge- and reasoning-based method for data-analytic city policymaking. Building Research & Information, 49(01), 38–54.

Kang, L, Liu, S, Zhang, H and Gong, D (2021) Person anomaly detection-based videos surveillance system in urban integrated pipe gallery. Building Research & Information, 49(01), 55–68.

  • Type: Journal Article
  • Keywords: Urban underground integrated pipe gallery; videos surveillance system; multiple-instance learning; person anomaly detection; AUC maximization; smart city;
  • ISBN/ISSN: 0961-3218
  • URL: https://doi.org/10.1080/09613218.2020.1779020
  • Abstract:
    The integrated pipe gallery, also known as urban lifeline, is a significant content of the smart city. While the video surveillance system is a crucial part of the integrated pipe gallery, which provides a basis for the construction of smart city. Due to the large amount of video data, manual monitoring is a time-consuming and laborious task. To address the above problems, we propose a neural network-based method that incorporates the concept of area under curve (AUC) with the multiple-instance learning (MIL) approach. We formulate the multiple-instance AUC (MIAUC) model that predicts high anomaly scores for anomalous segments. Furthermore, sparsity and temporal smoothness constraints are utilized in the loss function to better detect anomaly. To verify the effectiveness of our proposed method, a new database is established based on the video surveillance system, which consists of 110 real-world surveillance videos with a total length of 24 h. The experimental results on the real-world database show that our method achieves better performance as compared to the baselines methods. Moreover, we design a MIAUC-based video surveillance system and the practical effect reveals the prospect of utilizing the MIL method for person anomaly detection in the integrated pipe gallery.

Lee, Y L and Lee, Y (2021) Developing an autonomous psychological behaviour of virtual user to atypical architectural geometry. Building Research & Information, 49(01), 69–83.

Nuhu, B K, Aliyu, I, Adegboye, M A, Ryu, J K, Olaniyi, O M and Lim, C G (2021) Distributed network-based structural health monitoring expert system. Building Research & Information, 49(01), 144–59.

V E, S, Shin, C and Cho, Y (2021) Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city. Building Research & Information, 49(01), 127–43.

Valks, B, Arkesteijn, M H, Koutamanis, A and den Heijer, A C (2021) Towards a smart campus: supporting campus decisions with Internet of Things applications. Building Research & Information, 49(01), 1–20.