Assessment of human and transport traffic in densely populated Moscow areas using video from stationary cameras

Ruslan Vorobyev

Abstract


In this paper, the task is to obtain informative data using video analytic approaches based on computer vision and deep neural networks, as well as their further analytical evaluation in order to automate the process of calculating traffic in Moscow’s densely populated areas. An approach to solving this problem is proposed, allowing to calculate in the selected location two different traffic: human and automobile. A data set is needed for the neural network learning procedure. The accuracy of the algorithm is evaluated by measuring the quality measure of the detector. The paper deals with the task of determining (identifying) each individual object (person and vehicle) and further analyzing its movement. It lies in the fact that for a given video file is required to determine the number of people, vehicles moving in one of several predetermined directions. In the future, these data are analyzed (correlated with time intervals, help to identify "popular destinations", "hot zones" (zones of increased interest)), and on the basis of them build graphs and heat maps. This task relates to the field of detection of objects in the image


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References


Haykin S., Network N. A comprehensive foundation //Neural Networks. – 2004. – Т. 2. – №. 2004. – С. 41.

Krizhevsky A., Sutskever I., Hinton G. E. Imagenet classification with deep convolutional neural networks //Advances in neural information processing systems. – 2012. – С.1097-1105.

Girshick R. et al. Rich feature hierarchies for accurate object detection and semantic segmentation //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2014. – С.580-587.

Viola P., Jones M. Rapid object detection using a boosted cascade of simple features //Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. – IEEE, 2001. – Т. 1. – С. I-I.

Viola P., Jones M. Robust real-time object detection //International Journal of Computer Vision. – 2001. – Т. 4. – №. 34–47.

Szegedy C. et al. Going deeper with convolutions //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. – 2015. – С. 1-9.

Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition //arXiv preprint arXiv:1409.1556. – 2014.

Opelt A. et al. Weak hypotheses and boosting for generic object detection and recognition //Computer vision-ECCV 2004. – 2004. – С.71-84.

Lienhart R., Maydt J. An extended set of haar-like features for rapid object detection //Image Processing. 2002. Proceedings. 2002 International Conference on. – IEEE, 2002. – Т. 1. – С. I-I

Dalal N., Triggs B. Histograms of oriented gradients for human detection //Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. – IEEE, 2005. – Т. 1. – С. 886-893.

Suard F. et al. Pedestrian detection using histograms of oriented gradients //Intelligent Vehicles Symposium, 2006 IEEE. – IEEE, 2006. – С.206-212.

Lee H., Center S. Multiple Object Class Detection & Localization with Deep Learning (CNN). – 2015.

Buhler K., Lambert J., Vilim M. Real-time Object Tracking in Video CS 229 Course Project.

Redmon J., Farhadi A. YOLO9000: Better, Faster, Stronger //arXiv preprint arXiv:1612.08242. – 2016.

Redmon J. et al. You only look once: Unified, real-time object detection //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. – 2016. – С.779-788.

Girshick R. et al. Detectron. – 2018.

Redmon J., Farhadi A. YOLOv3: An incremental improvement //arXiv preprint arXiv:1804.02767. – 2018.

CV-Tricks. Zero to Hero: Guide to Object Detection using Deep Learning: Faster R-CNN, YOLO, SSD. cv-tricks.com/object-detection/faster-r-cnn-yolo-ssd/ 2018

Object Detection with Deep Learning: The Definitive Guide. tryolabs.com/blog/2017/08/30/object-detection-an-overview-in-the-age-of-deep-learning/ 2018

Girshick R. et al. Rich feature hierarchies for accurate object detection and semantic segmentation //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2014. – С.580-587.

Uijlings J. R. R. et al. Selective search for object recognition//International journal of computer vision. – 2013. – Т. 104. – №. 2. – С. 154-171.

Ren S. et al. Faster r-cnn: Towards real-time object detection with region proposal networks //Advances in neural information processing systems. – 2015. – С.91-99.

One-shot object detection. machinethink.net/blog/object-detection/ 2018

Liu W. et al. Ssd: Single shot multibox detector //European conference on computer vision. – Springer, Cham, 2016. – С.21-37.


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