Vehicle Type Detection by Convolutional Neural Networks
In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations.
KeywordsForeground detection Background modeling Convolutional neural networks Probabilistic self-organizing maps Background features
This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2014-53465-R, project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Andalusia (Spain) under projects TIC-6213, project name Development of Self-Organizing Neural Networks for Information Technologies; and TIC-657, project name Self-organizing systems and robust estimators for video surveillance. All of them include funds from the European Regional Development Fund (ERDF). Karl Thurnhofer-Hemsi is funded by a PhD scholarship from the Spanish Ministry of Education, Culture and Sport under the FPU program. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU.
- 4.Kato, N., Fadlullah, Z.M., Mao, B., Tang, F., Akashi, O., Inoue, T., Mizutani, K.: The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wirel. Commun. (2016)Google Scholar
- 5.Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25, pp. 1097–1105 (2012)Google Scholar
- 13.Sen-Ching, S.C., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Electronic Imaging 2004, pp. 881–892. International Society for Optics and Photonics (2004)Google Scholar
- 16.Wshah, S., Xu, B., Bulan, O., Kumar, J., Paul, P.: Deep learning architectures for domain adaptation in HOV/HOT lane enforcement. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–7 (2016)Google Scholar