Navegar

Colección

Datos Estadísticas

Conferencia

Orozco, I.; Buemi, M.E.; Berlles, J.J. "A study on Pedestrian detection using a deep convolutional neural network" (2016) International Conference on Pattern Recognition Systems, ICPRS 2016. 2016(2)
Estamos trabajando para incorporar este artículo al repositorio

Abstract:

Pedestrian detection is presently a topic of interest in computer vision due to its applications as an aid in car driving and in surveillance. The good results obtained using Convolutional Networks for vision tasks make them an attractive tool to improve the capabilities of pedestrian detection systems. In this work we study the use of a Convolutional Network as a refinement for classification of candidate regions previously detected using Haar features embedded in an AdaBoost scheme. The data used for training and testing come from the INRIA pedestrian database. The influence of design parameters, such as, the number of stages of the cascade in the detection stage and the scale factor in the pyramid of the multi-scale method, have been studied.

Registro:

Documento: Conferencia
Título:A study on Pedestrian detection using a deep convolutional neural network
Autor:Orozco, I.; Buemi, M.E.; Berlles, J.J.
Filiación:Departamento de Informática, Facultad de Ciencias Exactas, Universidad Nacional de Salta, Argentina
Departamento de Computación, FCEyN, Universidad de Buenos Aires, Argentina
Palabras clave:AdaBoost; Convolutional neural network; Deep learning; Haar-like features; Pedestrian detection; Adaptive boosting; Computer vision; Convolution; Face recognition; Feature extraction; Neural networks; Object detection; Pattern recognition systems; Convolutional networks; Convolutional neural network; Deep learning; Design parameters; Haar-like features; Pedestrian detection; Pedestrian detection system; Training and testing; Pattern recognition
Año:2016
Volumen:2016
Número:2
Título revista:International Conference on Pattern Recognition Systems, ICPRS 2016
Título revista abreviado:IET Semin Dig
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS16454_v2016_n2_p_Orozco

Referencias:

  • Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell, 34 (11), pp. 2189-2202. , November
  • Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I.J., Bergeron, A., Bouchard, N., Bengio, Y., Theano: New features and speed improvements (2012) Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop
  • Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Bengio, Y., Theano: A CPU and GPU math expression compiler (2010) Proceedings of the Python for Scientific Computing Conference (SciPy), , June Oral Presentation
  • Chen, X., Wei, P., Ke, W., Ye, Q., Jiao, J., (2015) Computer Vision-ACCV 2014 Workshops: Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part I, Chapter Pedestrian Detection with Deep Convolutional Neural Network, pp. 354-365. , Springer International Publishing, Cham
  • Dalal, N., Triggs, B., Histograms of oriented gradients for human detection (2005) International Conference on Computer Vision & Pattern Recognition, 2, pp. 886-893. , Cordelia Schmid, Stefano Soatto, and Carlo Tomasi, editors INRIA Rhône-Alpes, ZIRST-655, av. de l'Europe, Montbonnot-38334 June
  • Dollar, P., Appel, R., Belongie, S., Perona, P., Fast feature pyramids for object detection (2014) Pattern Analysis and Machine Intelligence IEEE Transactions on, 36 (8), pp. 1532-1545. , Aug
  • Dollár, P., Wojek, C., Schiele, B., Perona, P., Pedestrian detection: A benchmark (2009) CVPR, , June
  • Ess, A., Leibe, B., Schindler, K., Van Gool, L., A mobile vision system for robust multi-person tracking (2008) IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), , IEEE Press June
  • Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A., The pascal visual object classes (voc) challenge (2010) International Journal of Computer Vision, 88 (2), pp. 303-338. , June
  • Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D., Object detection with discriminatively trained part-based models (2010) IEEE Trans. Pattern Anal. Mach. Intell, 32 (9), pp. 1627-1645. , September
  • Geismann, P., Schneider, G., A two-staged approach to vision-based pedestrian recognition using haar and HOG features (2008) Intelligent Vehicles Symposium 2008 IEEE, pp. 554-559. , June
  • Lecun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradientbased learning applied to document recognition (1998) Proceedings of the IEEE, 86 (11), pp. 2278-2324. , Nov
  • Nam, W., Dollár, P., Hee Han, J., (2014) Local Decorrelation for Improved Detection, , CoRR, abs/1406.1134
  • Oren, M., Papageorgiou, C.P., Sinha, P., Osuna, E., Poggio, T., (1997) Pedestrian Detection Using Wavelet Templates, pp. 193-199
  • Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y., (2012) Pedestrian Detection with Unsupervised Multi-stage Feature Learning, , CoRR, abs/1212.0142
  • Tomè, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M., Tubaro, S., Deep convolutional neural networks for pedestrian detection (2015) Elsevier Journal of Signal Processing: Image Communication, , Submitted to abs/1510.03608
  • Uijlings, J.R.R., Van De Sande, K.E.A., Gevers, T., Smeulders, A.W.M., Selective search for object recognition (2013) International Journal of Computer Vision
  • Viola, P., Michael, J., Jones. Robust real-time face detection (2004) Int. J. Comput. Vision, 57 (2), pp. 137-154. , May
  • Zhu, Q., Yeh, M., Cheng, K., Avidan, S., Fast human detection using a cascade of histograms of oriented gradients Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Volume 2 CVPR '06, pp. 1491-1498. , Washington, DC, USA, 2006. IEEE Computer SocietyA4 -

Citas:

---------- APA ----------
Orozco, I., Buemi, M.E. & Berlles, J.J. (2016) . A study on Pedestrian detection using a deep convolutional neural network. International Conference on Pattern Recognition Systems, ICPRS 2016, 2016(2).
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS16454_v2016_n2_p_Orozco [ ]
---------- CHICAGO ----------
Orozco, I., Buemi, M.E., Berlles, J.J. "A study on Pedestrian detection using a deep convolutional neural network" . International Conference on Pattern Recognition Systems, ICPRS 2016 2016, no. 2 (2016).
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS16454_v2016_n2_p_Orozco [ ]
---------- MLA ----------
Orozco, I., Buemi, M.E., Berlles, J.J. "A study on Pedestrian detection using a deep convolutional neural network" . International Conference on Pattern Recognition Systems, ICPRS 2016, vol. 2016, no. 2, 2016.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS16454_v2016_n2_p_Orozco [ ]
---------- VANCOUVER ----------
Orozco, I., Buemi, M.E., Berlles, J.J. A study on Pedestrian detection using a deep convolutional neural network. IET Semin Dig. 2016;2016(2).
Available from: https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_NIS16454_v2016_n2_p_Orozco [ ]