Smart City Gnosys

Smart city article details

Title	Deep Learning For Pedestrian Collective Behavior Analysis In Smart Cities: A Model Of Group Trajectory Outlier Detection
ID_Doc	17880
Authors	Belhadi, A; Djenouri, Y; Srivastava, G; Djenouri, D; Lin, JCW; Fortino, G
Year	2021
Published	INFORMATION FUSION, 65
DOI	http://dx.doi.org/10.1016/j.inffus.2020.08.003
Abstract	This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database.
Author Keywords	Human behaviors; Deep learning; Data mining; Analysis; Smart cities

Similar Articles

Id	Similarity	Authors	Title	Published
19248	0.893	Alshamsi A.; Ali N.A.	Detection Of Anomalies In Crowds Using Smart City Infrastructure And Machine Learning	Proceedings - IEEE Global Communications Conference, GLOBECOM (2024)
17780	0.869	Sharif M.H.; Jiao L.; Omlin C.W.	Deep Crowd Anomaly Detection: State-Of-The-Art, Challenges, And Future Research Directions	Artificial Intelligence Review, 58, 5 (2025)
59770	0.866	Ameur I.; Ameur M.E.A.; Ameur M.; Dagha H.E.	Unveiling Human Activity Patterns In Smart Cities Through A Cnn-Lstm Approach	Lecture Notes in Networks and Systems, 1267 LNNS (2025)
8761	0.865	Khalifa O.O.; Roubleh A.; Esgiar A.; Abdelhaq M.; Alsaqour R.; Abdalla A.; Ali E.S.; Saeed R.	An Iot-Platform-Based Deep Learning System For Human Behavior Recognition In Smart City Monitoring Using The Berkeley Mhad Datasets	Systems, 10, 5 (2022)
28517	0.861	Lohrer A.; Binder J.J.; Kröger P.	Group Anomaly Detection For Spatio-Temporal Collective Behaviour Scenarios In Smart Cities	Proceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022 (2022)
57051	0.86	Chen X.; Lao X.; Kuan S.; Wang H.; Yang X.; Xie J.; Zhou J.	The Twin Terminal Of Pedestrian Trajectory Based On City Intelligent Model (Cim) 4.0	ACM International Conference Proceeding Series (2024)
12041	0.86	Adewopo V.; Elsayed N.; Elsayed Z.; Ozer M.; Zekios C.L.; Abdelgawad A.; Bayoumi M.	Big Data And Deep Learning In Smart Cities: A Comprehensive Dataset For Ai-Driven Traffic Accident Detection And Computer Vision Systems	2024 IEEE 3rd International Conference on Computing and Machine Intelligence, ICMI 2024 - Proceedings (2024)
12042	0.86	Adewopo V.; Elsayed N.; Elsayed Z.; Ozer M.; Zekios C.L.; Abdelgawad A.; Bayoumi M.	Big Data And Deep Learning In Smart Cities: A Comprehensive Dataset For Ai-Driven Traffic Accident Detection And Computer Vision Systems	Conference Proceedings - IEEE SOUTHEASTCON (2024)
50724	0.855	De Cock L.; Verstockt S.; Vandeviver C.; Van de Weghe N.	Smart Crowd Management: The Data, The Users And The Solution	Leibniz International Proceedings in Informatics, LIPIcs, 240 (2022)
26830	0.854	Cai Z.; Jiang R.; Lian X.; Yang C.; Wang Z.; Fan Z.; Tsubouchi K.; Kobayashi H.H.; Song X.; Shibasaki R.	Forecasting Citywide Crowd Transition Process Via Convolutional Recurrent Neural Networks	IEEE Transactions on Mobile Computing, 23, 5 (2024)