Detecting, tracking and counting people getting on/off a metropolitan train using a standard video camera
| Autor: | Alessandro Bay, Sergio A. Velastin, Jorge E. Espinosa, Rodrigo Fernandez |
|---|---|
| Přispěvatelé: | European Commission, Ministerio de Economía y Competitividad (España), Ministerio de Educación, Cultura y Deporte (España), Banco Santander |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Real-time computing Video Recording Video camera Transportation Camera sensor 02 engineering and technology camera sensor people detection people tracking lcsh:Chemical technology Biochemistry Field (computer science) Article Analytical Chemistry law.invention multi-object tracking law 0502 economics and business 11. Sustainability 0202 electrical engineering electronic engineering information engineering Multi-object tracking Humans People tracking lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Informática 050210 logistics & transportation business.industry Deep learning 05 social sciences People detection Traffic simulation deep learning Signal Processing Computer-Assisted Metropolitan area Atomic and Molecular Physics and Optics Motor Vehicles Public transport people counting 020201 artificial intelligence & image processing Artificial intelligence People counting business Algorithms |
| Zdroj: | e-Archivo: Repositorio Institucional de la Universidad Carlos III de Madrid Universidad Carlos III de Madrid (UC3M) e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 6251, p 6251 (2020) Sensors Volume 20 Issue 21 |
| Popis: | The main source of delays in public transport systems (buses, trams, metros, railways) takes place in their stations. For example, a public transport vehicle can travel at 60 km per hour between stations, but its commercial speed (average en-route speed, including any intermediate delay) does not reach more than half of that value. Therefore, the problem that public transport operators must solve is how to reduce the delay in stations. From the perspective of transport engineering, there are several ways to approach this issue, from the design of infrastructure and vehicles to passenger traffic management. The tools normally available to traffic engineers are analytical models, microscopic traffic simulation, and, ultimately, real-scale laboratory experiments. In any case, the data that are required are number of passengers that get on and off from the vehicles, as well as the number of passengers waiting on platforms. Traditionally, such data has been collected manually by field counts or through videos that are then processed by hand. On the other hand, public transport networks, specially metropolitan railways, have an extensive monitoring infrastructure based on standard video cameras. Traditionally, these are observed manually or with very basic signal processing support, so there is significant scope for improving data capture and for automating the analysis of site usage, safety, and surveillance. This article shows a way of collecting and analyzing the data needed to feed both traffic models and analyze laboratory experimentation, exploiting recent intelligent sensing approaches. The paper presents a new public video dataset gathered using real-scale laboratory recordings. Part of this dataset has been annotated by hand, marking up head locations to provide a ground-truth on which to train and evaluate deep learning detection and tracking algorithms. Tracking outputs are then used to count people getting on and off, achieving a mean accuracy of 92% with less than 0.15% standard deviation on 322 mostly unseen dataset video sequences. Sergio A. Velastin is grateful for funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement N 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander. Rodrigo Fernandez and Sergio A. Velastin gratefully acknowledge the Chilean National Science and Technology Council (Conicyt) for its funding under CONICYT-Fondecyt Regular Grant Nos. 1120219, 1080381 and 1140209 (“OBSERVE”). |
| Databáze: | OpenAIRE |
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