Pedestrian behavioral dynamics have been growingly investigated by means of (semi)automated computing techniques for almost two decades, exploiting advancements on computing power, sensor accuracy and availability, computer vision algorithms. This has led to a unique consensus on the existence of significant difference between uni-directional and bi-directional flows of pedestrians, where the phenomenon of lane formation seems to play a major role. This collective behavior emerges in condition of variable density and due to a self-organization dynamics, for which pedestrians are induced to walk following preceding persons to avoid and minimize conflictual situations. Although the formation of lanes is a well-known phenomenon in this field of study, there is still a lack of methods offering the possibility to provide an (even semi-)automatic identification and a quantitative characterization. In this context, the paper proposes an unsupervised learning approach for an automatic detection of lanes in multi-directional pedestrian flows, based on the DBSCAN clustering algorithm. The reliability of the approach is evaluated through a inter-agreement test between a human expert coder and the results of the automated analysis

Crociani, L., Vizzari, G., Gorrini, A., Bandini, S. (2018). Identification and Characterization of Lanes in Pedestrian Flows Through a Clustering Approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.71-82). Springer Verlag [10.1007/978-3-030-03840-3_6].

Identification and Characterization of Lanes in Pedestrian Flows Through a Clustering Approach

Crociani, Luca
Primo
;
Vizzari, Giuseppe
Secondo
;
Gorrini, Andrea
Penultimo
;
Bandini, Stefania
Ultimo
2018

Abstract

Pedestrian behavioral dynamics have been growingly investigated by means of (semi)automated computing techniques for almost two decades, exploiting advancements on computing power, sensor accuracy and availability, computer vision algorithms. This has led to a unique consensus on the existence of significant difference between uni-directional and bi-directional flows of pedestrians, where the phenomenon of lane formation seems to play a major role. This collective behavior emerges in condition of variable density and due to a self-organization dynamics, for which pedestrians are induced to walk following preceding persons to avoid and minimize conflictual situations. Although the formation of lanes is a well-known phenomenon in this field of study, there is still a lack of methods offering the possibility to provide an (even semi-)automatic identification and a quantitative characterization. In this context, the paper proposes an unsupervised learning approach for an automatic detection of lanes in multi-directional pedestrian flows, based on the DBSCAN clustering algorithm. The reliability of the approach is evaluated through a inter-agreement test between a human expert coder and the results of the automated analysis
paper
Analysis; Clustering; Lane formation; Pedestrian dynamics
English
Conference of the Italian Association for Artificial Intelligence, AI*IA 2018 20-23 November
2018
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
978-303003839-7
2018
11298
71
82
https://www.springer.com/series/558
none
Crociani, L., Vizzari, G., Gorrini, A., Bandini, S. (2018). Identification and Characterization of Lanes in Pedestrian Flows Through a Clustering Approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.71-82). Springer Verlag [10.1007/978-3-030-03840-3_6].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/218861
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