Pedestrian Age Estimation While Crossing Intersections Based on Deep Learning

Mohammed Talal Ghazal


The large-scale distribution of camera networks in the traffic area resulted in the increasing popularity of video surveillance systems. As pedestrian detection and tracking are the critical monitoring targets in traffic surveillance, many studies focus on pedestrian detection algorithms across cameras. This paper addressed the effect of using the age estimation based on deep convolution neural network (CNN) as a convenience for pedestrian monitoring who is crossing at intersections. Two popular Deep Convolutional Neural Networks (DCNNs) pre-trained models have been used in this work, which have recently achieved the best performance in facial features extraction tasks: VGG-Face and ResNet-50. We combined these two models to increase the efficiency of the proposed system. We ran our experiments to evaluate the system based on the VGGFace2 dataset consisting of 3.31 million face images. From the experimental results, we observed a gap in the detection performances between those age groups: children from (00-10) years and elderly with 55 years and more. Moreover, it noted that the proposed pedestrian age estimation model performance is high, also a good result can be obtained by using the model for new purpose.


Deep Learning,Convolutional Neural Networks,VGG-Face,ResNet-50,Pedestrian


World Health Organization, "Global status report on road safety," World Health Organization, 2018.

D. Duives, W. Daamen, and S. Hoogendoorn, "Monitoring the Number of Pedestrians in an Area: The Applicability of Counting Systems for Density State Estimation," Journal of Advanced Transportation, vol. 2018, 2018.

D. C. Schwebel, A. L. Davis, and E. E. O'Neal, "Child pedestrian injury: A review of behavioral risks and preventive strategies," American journal of lifestyle medicine, vol. 6, no. 4, pp. 292-302, 2012.

P. Patel and A. Thakkar, "The upsurge of deep learning for computer vision applications," International Journal of Electrical and Computer Engineering, vol. 10, no. 1, p. 538, 2020.

R. I. Bendjillali, M. Beladgham, K. Merit, and A. Taleb-Ahmed, "Illumination-robust face recognition based on deep convolutional neural networks architectures," Indonesian Journal of Electrical Engineering and Computer Science, vol. 18, no. 2, pp. 1015-1027, 2020.

D. A. Jasm, M. M. Murtadha, and A. T. H. Alrawi, " Deep image mining for convolution neural network," IAES International Journal of Artificial Intelligence, vol. 20, no. 1, p. 347-352, 2020.

H. Kim, S. Lee, and H. Jung, "Human activity recognition by using convolutional neural network," International Journal of Electrical and Computer Engineering, vol. 9, no. 6, p. 5270, 2019.

B. Kim, N. Yuvaraj, K. Sri Preethaa, R. Santhosh, and A. Sabari, "Enhanced pedestrian detection using optimized deep convolution neural network for smart building surveillance," Soft Computing, pp. 1-12, 2020.

F. H. K. Zaman, J. Johari, and A. I. M. Yassin, " Learning Face Similarities for Face Verification using Hybrid Convolutional Neural Networks," IAES International Journal of Artificial Intelligence, vol. 16, no. 3, p. 1333-1342, 2019.

R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," Insights into imaging, vol. 9, no. 4, pp. 611-629, 2018.

Z. Kadim, M. A. Zulkifley, and N. Hamzah, "Deep-learning based single object tracker for night surveillance," International Journal of Electrical & Computer Engineering (2088-8708), vol. 10, 2020.

N. O'Mahony et al., "Deep learning vs. traditional computer vision," in Science and Information Conference, 2019: Springer, pp. 128-144.

M. H. A. Hijazi, S. K. T. Hwa, A. Bade, R. Yaakob, and M. S. Jeffree, "Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images," IAES International Journal of Artificial Intelligence, vol. 8, no. 4, p. 429, 2019.

A. Dhomne, R. Kumar, and V. Bhan, "Gender recognition through face using deep learning," Procedia Computer Science, vol. 132, pp. 2-10, 2018.

Z. Qawaqneh, A. A. Mallouh, and B. D. Barkana, "Deep convolutional neural network for age estimation based on VGG-face model," arXiv preprint arXiv:1709.01664, 2017.

M. H. Zaki and T. Sayed, "Automated analysis of pedestrians' nonconforming behavior and data collection at an urban crossing," Transportation research record, vol. 2443, no. 1, pp. 123-133, 2014.

D. Ka, D. Lee, S. Kim, and H. Yeo, "Study on the framework of intersection pedestrian collision warning system considering pedestrian characteristics," Transportation research record, vol. 2673, no. 5, pp. 747-758, 2019.

N. Wojke, A. Bewley, and D. Paulus, "Simple online and realtime tracking with a deep association metric," in 2017 IEEE international conference on image processing (ICIP), 2017: IEEE, pp. 3645-3649.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580-587.

R. Rothe, R. Timofte, and L. Van Gool, "Dex: Deep expectation of apparent age from a single image," in Proceedings of the IEEE international conference on computer vision workshops, 2015, pp. 10-15.

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.

O. M. Parkhi, A. Vedaldi, and A. Zisserman, "Deep face recognition," 2015.

 ISSN: 2502-4752

Indonesian J Elec Eng & Comp Sci, Vol. 15, No. 3, September 2020 : xx - xx

A. A. Moustafa, A. Elnakib, and N. F. Areed, "Optimization of deep learning features for age-invariant face recognition," International Journal of Electrical & Computer Engineering (2088-8708), vol. 10, 2020.

Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "Vggface2: A dataset for recognising faces across pose and age," in 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018: IEEE, pp. 67-74.

O. Russakovsky et al., "Imagenet large scale visual recognition challenge," International journal of computer vision, vol. 115, no. 3, pp. 211-252, 2015.

H. Sofian, J. T. C. Ming, S. Muhammad, and N. M. Noor, "Calcification detection using convolutional neural network architectures in intravascular ultrasound images," Indonesian Journal of Electrical Engineering and Computer Science, vol. 17, no. 3, pp. 1313-1321, 2020.

R. F. Rahmat, O. S. Sitompul, S. Purnamawati, and R. Budiarto, "Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network," Telkomnika, vol. 17, no. 5, pp. 2659-2666, 2019.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics