Traffic Flow Classification using a Video Descriptor and a Convolutional Neural Network
Resumo
Traffic congestion is a significant problem in urban cities and affects economic, health, and social questions. Although many works have been published in the last years to traffic applications based on video data, different techniques of computer vision can be explored in this area. In this work, we proposed a method for traffic flow classification using StarRGB and Convolutional Neural Networks (CNN). The StarRGB describes a global representation of the traffic video into a colored image based on motion elements in the scene. Then, the generated image passed as input to a pre-trained CNN to extract the features and classify the traffic video activity in three classes: LIGHT, MEDIUM, and HEAVY. In our experiments using a traffic video database, the proposed method reached an accuracy of 96.47%. Also, the results suggest that StarRGB is a good descriptor for traffic video applications.
Palavras-chave:
Computer Vision, StarRGB, CNN, Traffic Flow
Classification
Referências
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P. Chakraborty, Y. O. Adu-Gyamfi, S. Poddar, V. Ahsani, A. Sharma, and S. Sarkar, Traffic congestion detection from camera images using deep convolution neural networks, Transportation Research Record.
L.-P. Beland and D. A. Brent, Traffic and crime, Journal of Public Economics, vol. 160, pp. 96 - 116, 2018.
A. M. de Souza, C. A. Brennand, R. S. Yokoyama, E. A. Donato, E. R. Madeira, and L. A. Villas, Traffic management systems: A classification, review, challenges, and future perspectives, International Journal of Distributed Sensor Networks, vol. 13, no. 4, p. 1550147716683612, 2017.
P. Borkar and L. G. Malik, Review on vehicular speed, density estimation and classification using acoustic signal. International Journal for Traffic & Transport Engineering, vol. 3, no. 3, 2013.
N. Lefebvre, X. Chen, P. Beauseroy, and M. Zhu, Traffic flow estimation using acoustic signal, Engineering Applications of Artificial Intelligence, vol. 64, pp. 164 - 171, 2017. DOI: 10.1016/j.engappai.2017.05.019
J. Chung and K. Sohn, Image-based learning to measure traffic density using a deep convolutional neural network, IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 5, pp. 1670 - 1675, May 2018. DOI: 10.1109/tits.2017.2732029
O. Asmaa, K. Mokhtar, and O. Abdelaziz, Road traffic density estimation using microscopic and macroscopic parameters, Image and Vision Computing, vol. 31, no. 11, pp. 887 - 894, 2013. DOI: 10.1016/j.imavis.2013.09.006
N. Buch, S. A. Velastin, and J. Orwell, A review of computer vision techniques for the analysis of urban traffic, Trans. Intell. Transport. Sys., vol. 12, no. 3, pp. 920 - 939, Sep. 2011. DOI: 10.1109/tits.2011.2119372
R. Loce, R. Bala, and M. Trivedi, Computer Vision and Imaging in Intelligent Transportation Systems, 04 2017.
L. Wei and D. Hong-Ying, Real-time road congestion detection based on image texture analysis, Procedia engineering, vol. 137, pp. 196 - 201, 2016. DOI: 10.1016/j.proeng.2016.01.250
A. B. Chan and N. Vasconcelos, Classification and retrieval of traffic video using auto-regressive stochastic processes, in Intelligent Vehicles Symposium, Proceedings. IEEE. IEEE, 2005, pp. 771 - 776. DOI: 10.1109/ivs.2005.1505198
T. Pamula, Road traffic conditions classification based on multilevel filtering of image content using convolutional neural networks, IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 3, pp. 11 - 21, 2018.
M. Manana, C. Tu, and P. A. Owolawi, A survey on vehicle detection based on convolution neural networks, in Computer and Communications (ICCC), 2017 3rd IEEE International Conference on. IEEE, 2017, pp. 1751 - 1755.
C. C. dos Santos, J. L. A. Samatelo, and R. F. Vassallo, Dynamic gesture recognition by using cnns and star rgb: a temporal information condensation, CoRR, vol. abs/ 08505, 2019.
L. O. Andrews Sobral, L. Schnitman, and F. De Souza, Highway traffic congestion classification using holistic properties, in 10th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, 2013.
S. Hu, J. Wu, and L. Xu, Real-time traffic congestion detection based on video analysis, Journal of Information and Computational Science, vol. 9, no. 10, pp. 2907 - 2914, 2012, cited By 14.
Z. Luo, P.-M. Jodoin, S.- Z. Su, S.- Z. Li, and H. Larochelle, Traffic analytics with low-frame-rate videos, IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 4, pp. 878 - 891, 2018.
Z. Luo, P.-M. Jodoin, S.- Z. Li, and S.-Z. Su, Traffic analysis without motion features, in 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015, pp. 3290 - 3294. DOI: 10.1109/icip.2015.7351412
K. G. Derpanis and R. P. Wildes, Classification of traffic video based on a spatiotemporal orientation analysis, in Applications of Computer Vision (WACV), 2011 IEEE Workshop on. IEEE, 2011, pp. 606 - 613.
Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, nature, vol. 521, no. 7553, p. 436, 2015. DOI: 10.1038/nature14539
D. Jo, B. Yu, H. Jeon, and K. Sohn, Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies, IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1188 - 1197, 2019.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., Imagenet large scale visual recognition challenge, International Journal of Computer Vision, vol. 115, no. 3, pp. 211 - 252, 2015.
K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, 2014.
P. Barros, G. I. Parisi, D. Jirak, and S. Wermter, Real-time gesture recognition using a humanoid robot with a deep neural architecture, in 2014 IEEE-RAS International Conference on Humanoid Robots. IEEE, 2014, pp. 646 - 651. DOI: 10.1109/humanoids.2014.7041431
J. L. A. Samatelo and E. O. T. Salles, A new change detection algorithm for visual surveillance system, IEEE Latin America Transactions, vol. 10, no. 1, pp. 1221 - 1226, 2012. DOI: 10.1109/tla.2012.6142465
A. Riaz and S. A. Khan, “Traffic congestion classification using motion vector statistical features,” in Sixth International Conference on Machine Vision (ICMV 2013), vol. 9067. International Society for Optics and Photonics, 2013, p. 90671A.
Publicado
09/09/2019
Como Citar
RIBEIRO, Matheus Vieira Lessa; SAMATELO, Jorge Leonid Aching.
Traffic Flow Classification using a Video Descriptor and a Convolutional Neural Network. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 15. , 2019, São Bernardo do Campo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 137-142.
DOI: https://doi.org/10.5753/wvc.2019.7642.
