Vehicle speed estimation using audio features and neural networks
Many car accidents that result in pedestrian deaths or serious injuries are due to their inattention when crossing the street. Pedestrians often get distracted using mobile phones or music players, what prevents them to perceive warning signs and sounds. In this work, we developed a method to estima...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2017
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8940
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8940
- Palabra clave:
- Accidents
Acoustic signals
Audio features
Frequency and time domains
Hidden layers
Music players
Serious injuries
Speed estimation
Training algorithms
Pedestrian safety
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Vehicle speed estimation using audio features and neural networks |
| title |
Vehicle speed estimation using audio features and neural networks |
| spellingShingle |
Vehicle speed estimation using audio features and neural networks Accidents Acoustic signals Audio features Frequency and time domains Hidden layers Music players Serious injuries Speed estimation Training algorithms Pedestrian safety |
| title_short |
Vehicle speed estimation using audio features and neural networks |
| title_full |
Vehicle speed estimation using audio features and neural networks |
| title_fullStr |
Vehicle speed estimation using audio features and neural networks |
| title_full_unstemmed |
Vehicle speed estimation using audio features and neural networks |
| title_sort |
Vehicle speed estimation using audio features and neural networks |
| dc.subject.keywords.none.fl_str_mv |
Accidents Acoustic signals Audio features Frequency and time domains Hidden layers Music players Serious injuries Speed estimation Training algorithms Pedestrian safety |
| topic |
Accidents Acoustic signals Audio features Frequency and time domains Hidden layers Music players Serious injuries Speed estimation Training algorithms Pedestrian safety |
| description |
Many car accidents that result in pedestrian deaths or serious injuries are due to their inattention when crossing the street. Pedestrians often get distracted using mobile phones or music players, what prevents them to perceive warning signs and sounds. In this work, we developed a method to estimate the speed of an approaching vehicle using features of the generated acoustic signals. This system can be used as a component of a warning system of potential road risks for pedestrians. We used a single microphone to record audio signals. They were processed to extract features in frequency and time domains that were used as inputs to a neural network. Speed estimation was done using a feed forward neural network. We used several architectures and training algorithms. Results show mean error percentages of 14.57% for speeds from 10 to 40 km/h when using a neural network with two hidden layers. © 2016 IEEE. |
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2017 |
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2017 |
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2020-03-26T16:32:38Z |
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2020-03-26T16:32:38Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Conferencia |
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Proceedings of the 2016 IEEE ANDESCON, ANDESCON 2016 |
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9781509025312 |
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https://hdl.handle.net/20.500.12585/8940 |
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10.1109/ANDESCON.2016.7836250 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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56520286300 24329839300 57210822856 |
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Proceedings of the 2016 IEEE ANDESCON, ANDESCON 2016 9781509025312 10.1109/ANDESCON.2016.7836250 Universidad Tecnológica de Bolívar Repositorio UTB 56520286300 24329839300 57210822856 |
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https://hdl.handle.net/20.500.12585/8940 |
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eng |
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eng |
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19 October 2016 through 21 October 2016 |
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Institute of Electrical and Electronics Engineers Inc. |
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2020-03-26T16:32:38Z2020-03-26T16:32:38Z2017Proceedings of the 2016 IEEE ANDESCON, ANDESCON 20169781509025312https://hdl.handle.net/20.500.12585/894010.1109/ANDESCON.2016.7836250Universidad Tecnológica de BolívarRepositorio UTB565202863002432983930057210822856Many car accidents that result in pedestrian deaths or serious injuries are due to their inattention when crossing the street. Pedestrians often get distracted using mobile phones or music players, what prevents them to perceive warning signs and sounds. In this work, we developed a method to estimate the speed of an approaching vehicle using features of the generated acoustic signals. This system can be used as a component of a warning system of potential road risks for pedestrians. We used a single microphone to record audio signals. They were processed to extract features in frequency and time domains that were used as inputs to a neural network. Speed estimation was done using a feed forward neural network. We used several architectures and training algorithms. Results show mean error percentages of 14.57% for speeds from 10 to 40 km/h when using a neural network with two hidden layers. © 2016 IEEE.IEEE Peru SectionRecurso electrónicoapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85015226955&doi=10.1109%2fANDESCON.2016.7836250&partnerID=40&md5=f9ffd2bccf30cac1571f2e65300bc7b5Scopus2-s2.0-850152269552016 IEEE ANDESCON, ANDESCON 2016Vehicle speed estimation using audio features and neural networksinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fAccidentsAcoustic signalsAudio featuresFrequency and time domainsHidden layersMusic playersSerious injuriesSpeed estimationTraining algorithmsPedestrian safety19 October 2016 through 21 October 2016Giraldo-Guzmán J.Marrugo A.G.Contreras Ortiz, Sonia HelenaAshmead, D.H., Grantham, D.W., Maloff, E.S., Hornsby, B., Nakamura, T., Davis, T.J., Pampel, F., Rushing, E.G., Auditory perception of motor vehicle travel paths (2012) Human Factors: The Journal of the Human Factors and Ergonomics Society, 54 (3), pp. 437-453Emerson, R.W., Naghshineh, K., Hapeman, J., Wiener, W., A pilot study of pedestrians with visual impairments detecting traffic gaps and surges containing hybrid vehicles (2011) Transportation Research Part F: Traffic Psychology and Behaviour, 14 (2), pp. 117-127Pfeffer, K., Barnecutt, P., Children's auditory perception of movement of traffic sounds (1996) Child: Care, Health and Development, 22 (2), pp. 129-137Schwebel, D.C., Stavrinos, D., Byington, K.W., Davis, T., O'Neal, E.E., De Jong, D., Distraction and pedestrian safety: How talking on the phone, texting, and listening to music impact crossing the street (2012) Accident Analysis & Prevention, 45, pp. 266-271Stelling-Konczak, A., Hagenzieker, M., Van Wee, G., Cycling and sounds: The impact of the use of electronic devices on cycling safety (2013) Proceedings of the 3rd International Conference on Driver Distraction and Inattention, , Gothenburg, Sweden, 4-6 SeptemberOrganization, W.H., (2013) Global Status Report on Road Safety 2013: Supporting A Decade of Action: SummaryThomas, D., Wilkins, B., Determination of engine firing rate from the acoustic waveform (1970) Electronics Letters, 7 (6), pp. 193-194Boashash, B., O'Shea, P., A methodology for detection and classification of some underwater acoustic signals using time-frequency analysis techniques (1990) IEEE Transactions on Acoustics, Speech, and Signal Processing, 38 (11), pp. 1829-1841Cevher, V., Chellappa, R., McClellan, J.H., Vehicle speed estimation using acoustic wave patterns (2009) IEEE Transactions on Signal Processing, 57 (1), pp. 30-47Koops, H.V., Franchetti, F., (2015) An Ensemble Technique for Estimating Vehicle Speed and Gear Position from Acoustic Data, pp. 422-426Dogan, S., Temiz, M.S., Külür, S., Real time speed estimation of moving vehicles from side view images from an uncalibrated video camera (2010) Sensors, 10 (5), pp. 4805-4824Saqib, M., Khan, S.D., Basalamah, S.M., Techniques to estimate vehicle speed International Conference on Advanced Communications and Computation, (C), pp. 98-102Jhumat, S., Purwar, R.K., Techniques to estimate vehicle speed International Journal Od Advanced Research in Computer and Communication Engineering, (6), pp. 6875-6878Kassem, N., Kosba, A.E., Youssef, M., (2012) Rf-based Vehicle Detection and Speed Estimation, pp. 1-5Li, H., Dong, H., Jia, L., Xu, D., Qin, Y., (2011) Some Practical Vehicle Speed Estimation Methods by A Single Traffic Magnetic Sensor, pp. 1566-1573Jacobsen, E., Kootsookos, P., Fast, accurate frequency estimators (2007) IEEE Signal Process. Mag, 24 (3), pp. 123-125http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8940/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8940oai:repositorio.utb.edu.co:20.500.12585/89402023-05-26 10:33:31.358Repositorio Institucional UTBrepositorioutb@utb.edu.co |