Mostrar el registro sencillo del documento

Conteo de vehículos a partir de vídeos usando machine learning

dc.rights.licenseAtribución-SinDerivadas 4.0 Internacional
dc.contributor.advisorPedraza Bonilla, Cesar Augusto
dc.contributor.authorNavarro Ávila, Juan Sebastián
dc.date.accessioned2020-05-12T01:53:08Z
dc.date.available2020-05-12T01:53:08Z
dc.date.issued2020-02-13
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/77510
dc.description.abstractThis work presents a framework for vehicle counting from videos, using deep neural networks as detectors. The framework has 4 stages: preprocessing, detection and classification, tracking, and post-processing. For the detection stage, several deep object detector are compared and 3 new ones are proposed based on Tiny YOLOv3. For the tracking, a new tracker based on IOU is compared against the classic ones: Boosting, KCF, TLD, Mediaflow, MOSSE and CSRT. The comparison is based on 8 multi-object tracking metrics over the Bog19 dataset. The Bog19 dataset is a collection of annotated videos from the city of Bogota. The annotations include bicycles, buses, cars, motorbikes and trucks. Finally, the system is evaluated for the task of vehicle counting on this dataset. For the counting task, the combinations of the proposed detectors with the Medianflow and MOSSE trackers obtain the best results. The founded detectors have the same performance as those of the state of the art but with a higher speed.
dc.description.abstractEste trabajo presenta un framework para el conteo de vehı́culos a partir de videos, utilizando redes neuronales profundas como detectores. El framework tiene 4 etapas: preprocesamiento, detección y clasificación, seguimiento y post-procesamiento. Para la etapa de detección se comparan varios detectores de objetos profundos y se proponen 3 nuevos basados en Tiny YOLOv3. Para el rastreo, se compara un nuevo rastreador basado en IOU con los clásicos: Boosting, KCF, TLD, Mediaflow, MOSSE y CSRT. La comparación se hace en base a 8 métricas de seguimiento multiobjeto sobre el conjunto de datos del Bog19. El conjunto de datos Bog19 es una colección de videos anotados de la ciudad de Bogotá. Las clases de objetos anotados incluyen bicicletas, autobuses, coches, motos y camiones. Finalmente el sistema es evaluado para la tarea de contar vehı́culos en este conjunto de datos. Para la tarea de conteo, las combinaciones de los detectores propuestos y los rastreadores Medianflow y MOSSE obtienen los mejores resultados. Los detectores encontrados tienen el mismo desempeño que los del estado del arte pero con una mayor velocidad.
dc.format.extent60
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subject.ddc620 - Ingeniería y operaciones afines
dc.titleConteo de vehículos a partir de vídeos usando machine learning
dc.typeOtro
dc.rights.spaAcceso abierto
dc.description.additionalMagíster en Ingeniería - Ingeniería de Sistemas y Computación
dc.type.driverinfo:eu-repo/semantics/other
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.description.degreelevelMaestría
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
dc.relation.referencesN. Buch, S. Velastin, and J. Orwell, “A review of computer vision techniques for the analysis of urban traffic,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 920–939, 2011. cited By 171. “Citilog,” Nov. 2016. E. TIEMPO, “Bogotá tendrá nuevo centro de control para vigilar el tráfico,” EL TIEMPO, Dec. 2015. K.-S. Jie and M. Liu, “Computer vision based real-time information acquisition for transport traffic,” in ICIA 2005 - Proceedings of 2005 International Conference on Information Acquisition, vol. 2005, (Hong Kong), pp. 164–169, 2005. A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, vol. 2, pp. 1097–1105, 2012. Y. Liu, B. Tian, S. Chen, F. Zhu, and K. Wang, “A survey of vision-based vehicle detection and tracking techniques in ITS,” in Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2013, pp. 72–77, 2013. cited By 3. E. Baş, A. b. b. Tekalp, and F. Salman, “Automatic vehicle counting from video for traffic flow analysis,” in IEEE Intelligent Vehicles Symposium, Proceedings, (Istanbul), pp. 392–397, 2007. L. Li, W. Huang, I. Gu, and Q. Tian, “Foreground object detection from videos containing complex background,” in Proceedings of the ACM International Multimedia Conference and Exhibition, (Berkeley, CA.), pp. 2–10, 2003. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, “Focal Loss for Dense Object Detection,” in Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-October, pp. 2999–3007, Institute of Electrical and Electronics Engineers Inc., 2017. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation, vol. 1, no. 4, pp. 541–551, 1989. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001, vol. 1, pp. I–511–I–518 vol.1, 2001. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 886–893, 2005. P. Druzhkov and V. Kustikova, “A survey of deep learning methods and software tools for image classification and object detection,” Pattern Recognition and Image Analysis, vol. 26, no. 1, pp. 9–15, 2016. R. Girshick, “Fast R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision, vol. 11-18-December-2015, pp. 1440–1448, Institute of Electrical and Electronics Engineers Inc., 2015. cited By 23; Conference of 15th IEEE International Conference on Computer Vision, ICCV 2015 ; Conference Date: 11 December 2015 Through 18 December 2015; Conference Code:119541. S. b. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems (G. R. L. N. S. M. Cortes C., Lee D.D., ed.), vol. 2015-January, pp. 91–99, Neural information processing systems foundation, 2015. cited By 75; Conference of 29th Annual Conference on Neural Information Processing Systems, NIPS 2015 ; Conference Date: 7 December 2015 Through 12 December 2015; Conference Code:120037. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. Berg, “SSD: Single shot multibox detector,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9905 LNCS, pp. 21–37, 2016. A. Wong, M. Shafiee, F. Li, and B. Chwyl, “Tiny SSD: A tiny single-shot detection deep convolutional neural network for real-time embedded object detection,” in Proceedings - 2018 15th Conference on Computer and Robot Vision, CRV 2018, pp. 95–101, 2018. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-January, pp. 779–788, 2016. J. Redmon and A. Farhadi, “YOLO9000: Better, faster, stronger,” in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-January, pp. 6517–6525, 2017. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” CoRR, vol. abs/1804.02767, 2018. Q. Fan, L. Brown, and J. Smith, “A closer look at Faster R-CNN for vehicle detection,” in IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2016-August, pp. 124–129, Institute of Electrical and Electronics Engineers Inc., 2016. cited By 0; Conference of 2016 IEEE Intelligent Vehicles Symposium, IV 2016 ; Conference Date: 19 June 2016 Through 22 June 2016; Conference Code:123227. Wahyono, V. D. Hoang, L. Kurnianggoro, and K. H. Jo, “Scalable histogram of oriented gradients for multi-size car detection,” in 2014 10th France-Japan/ 8th Europe-Asia Congress on Mecatronics (MECATRONICS), pp. 228–231, Nov. 2014. B. Morris and M. Trivedi, “Robust classification and tracking of vehicles in traffic video streams,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, (Toronto, ON), pp. 1078–1083, 2006. cited By 28; Conference of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference ; Conference Date: 17 September 2006 Through 20 September 2006; Conference Code:71696. R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Journal of Basic Engineering, vol. 82, pp. 35–45, Mar. 1960. T. Mauthner, M. Donoser, and H. Bischof, “Robust tracking of spatial related components,” in Proceedings - International Conference on Pattern Recognition, (Tampa, FL), 2008. cited By 8; Conference of 2008 19th International Conference on Pattern Recognition, ICPR 2008 ; Conference Date: 8 December 2008 Through 11 December 2008; Conference Code:81859. F. Bardet and T. Chateau, “MCMC particle filter for real-time visual tracking of vehicles,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, (Beijing), pp. 539–544, 2008. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV), vol. 115, no. 3, pp. 211–252, 2015. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes (VOC) Challenge,” International Journal of Computer Vision, vol. 88, pp. 303–338, June 2010. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common Objects in Context,” Lecture Notes in Computer Science, pp. 740–755, 2014. L. Wen, D. Du, Z. Cai, Z. Lei, M.-C. Chang, H. Qi, J. Lim, M.-H. Yang, and S. Lyu, “DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking,” arXiv CoRR, vol. abs/1511.04136, 2015. P. Zhu, L. Wen, X. Bian, L. Haibin, and Q. Hu, “Vision Meets Drones: A Challenge,” arXiv preprint arXiv:1804.07437, 2018. P. Voigtlaender, M. Krause, A. Osep, J. Luiten, B. B. G. Sekar, A. Geiger, and B. Leibe, “MOTS: Multi-Object Tracking and Segmentation,” in Conference on Computer Vision and Pattern Recognition (CVPR), 2019. Y. c. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, 2016. A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” ACM Computing Surveys, vol. 38, no. 4, 2006. G. Ciaparrone, F. Luque Sánchez, S. Tabik, L. Troiano, R. Tagliaferri, and F. Herrera, “Deep learning in video multi-object tracking: A survey,” Neurocomputing, vol. 381, pp. 61–88, 2020. E. Bochinski, V. Eiselein, and T. Sikora, “High-Speed tracking-by-detection without using image information,” in 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017, 2017. E. Bochinski, T. Senst, and T. Sikora, “Extending IOU Based Multi-Object Tracking by Visual Information,” in Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2019. E. E. Hilbert, P. A. Rennie, and W. A. Kneidl, “A sensor for control of arterials and networks,” IEEE Transactions on Vehicular Technology, vol. 29, pp. 208–215, May 1980. J.-W. b. Hsieh, S.-H. b. Yu, Y.-S. b. Chen, and W.-F. b. Hu, “Automatic traffic surveillance system for vehicle tracking and classification,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 2, pp. 179–186, 2006. cited By 169. Y. b. Lecun, Y. Bengio, and G. e. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. F. Valcárcel, C. Chaves, R. Moreno, and O. Sanchez, “Machine vision algorithms applied to dynamic traffic light control [Algoritmos de visión de máquina aplicados al control dinamico de intersecciones semáforizadas],” DYNA (Colombia), vol. 80, no. 178, pp. 132–140, 2013. cited By 0. C. Pang, W. Lam, and N. Yung, “A method for vehicle count in the presence of multiple-vehicle occlusions in traffic images,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 3, pp. 441–459, 2007. C. Vondrick, D. Patterson, and D. Ramanan, “Efficiently scaling up crowdsourced video annotation,” International journal of computer vision, vol. 101, no. 1, pp. 184–204, 2013.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalVehicle
dc.subject.proposalVehı́culo
dc.subject.proposalVideo analysis
dc.subject.proposalAnálisis de video
dc.subject.proposalAprendizaje de maquina
dc.subject.proposalMachine learning
dc.subject.proposalComputer vision
dc.subject.proposalVisión por computador
dc.subject.proposalAprendizaje profundo
dc.subject.proposalDeep learning
dc.subject.proposalObject detection
dc.subject.proposalDetección de objectos
dc.subject.proposalRastreo de objetos
dc.subject.proposalObject tracking
dc.type.coarhttp://purl.org/coar/resource_type/c_1843
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


Archivos en el documento

Thumbnail
Nombre:
license_rdf
Tamaño:
805bytes
Formato:
application/rdf+xml
Descargar
Thumbnail
Nombre:
Trabajo final jsnavarroa vvc.pdf
Tamaño:
3.001Mb
Formato:
PDF
Descargar

Este documento aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del documento

Atribución-SinDerivadas 4.0 InternacionalEsta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial 4.0.Este documento ha sido depositado por parte de el(los) autor(es) bajo la siguiente constancia de depósito