Video Surveillance System with a Deep Learning Approach

Main Article Content

Puji Lestari David Hamonangan D. Manik Nurseve Lina Br Sihotang Amir Mahmud Husein

Abstract

Abstract— The application of in-depth learning methods has been successfully applied in computer vision task with the ability to learn the features of differences in real world images by directly from the original image by passing layer after layer to get the high dimensions image, in this study we applied the YOLO method approach with network adaptation features based on Darknet-53 on a video dataset recorded by the activities of University of Indonesia Prima (UNPRI) students with are conditions of video with different objects as a surveillance system, based on the results of research into object classification produces an overall accuracy of 93%, but for the classification of objects bikes, buses, and cars have the lowest accuracy of 30% for bikes, 54% of cars and buses by 40% so it is necessary to develop methods to improve accuracy.

Downloads

Download data is not yet available.

Article Details

How to Cite
LESTARI, Puji et al. Video Surveillance System with a Deep Learning Approach. SinkrOn, [S.l.], v. 4, n. 1, p. 263-267, oct. 2019. ISSN 2541-2019. Available at: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10247>. Date accessed: 13 nov. 2019. doi: https://doi.org/10.33395/sinkron.v4i1.10247.
Section
Articles
**************** Abstract viewed = 0 times ****************

References

Chakraborty, P., Adu-Gyamfi, Y. O., Poddar, S., Ahsani, V., Sharma, A., & Sarkar, S. (2018). Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks. Journal of the Transportation Research Board, 222-231. doi:https://doi.org/10.1177/0361198118777631

Chauhan, M. S., Singh, A., Khemka, M., Prateek, A., & Sen, R. (2019). Embedded CNN based vehicle classification and counting in non-laned road traffic. ArXIV, 1-10.

Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., & Berg, A. C. (2017, January 23). Archive. Retrieved from Archive Cornell University: https://arxiv.org/abs/1701.06659

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2018). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-10). Columbus, OH, USA: IEEE.

Harahap, M., Husein, A. M., & Dharma, A. (2017). Identifikasi Tanda Tangan Dengan Kohonen Som Berbasis Principal Component Analysis. Seminar Nasional APTIKOM (SEMNASTIKOM) 3, (pp. 333-337). Medan.

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (pp. 10-15). -: IEEE.

Huang, R., Pedoeem, J., & Chen, C. (2018). YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. IEEE International Conference on Big Data (Big Data) (pp. 2503–2510). Seattle, WA, USA, USA: IEEE. doi:https://doi.org/10.1109/BigData.2018.8621865

Husein, A. M., & Harahap, M. (2017). Penerapan Metode Distance Transform pada Kernel Discriminant Analysis untuk Pengenalan Pola Tulisan Tangan Angka Berbasis Principal Component Analysis. SinkrOn, 31-36.

Husein, A. M., & Harahap, M. (2017). Pengenalan Multi Wajah Berdasarkan Klasifikasi Kohonen SOM Dioptimalkan dengan Algoritma Discriminant Analysis PCA. Query: Journal of Information Systems, 33-39.

Lan, W., Dang, J., Wang, Y., & Wang, S. (2018). Pedestrian Detection Based on YOLO Network Model. IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 123-126). Changchun, China: IEEE. doi:https://doi.org/10.1109/ICMA.2018.8484698

Li, X., Liu, Y., Zhao, Z., Zhang, Y., & He, L. (2018). A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video. Journal of Advanced Transportation, 1-11. doi:https://doi.org/10.1155/2018/7075814
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single Shot MultiBox Detector. European Conference on Computer Vision (pp. 21-37). Amsterdam, The Netherlands: Springer.

Liu, Z., Chen, Z., Li, Z., & Hu, W. (2018). An Efficient Pedestrian Detection Method Based on YOLOv2. Mathematical Problems in Engineering, 1-10.

Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. New York, Ithaca, New York.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 112-128). Las Vegas, NV, USA: IEEE. doi:https://doi.org/10.1109/CVPR.2016.91

Ren, S., He, K., & Girshick, R. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (pp. 1137–1149). IEEE. doi:https://doi.org/10.1109/TPAMI.2016.2577031

Ren, Y., Zhu, C., & Xiao, S. (2018). Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images. Remote Sensing, 56-66.
Ross, G. (2015). Fast R-CNN. IEEE International Conference on Computer Vision (ICCV) (pp. 1-10). Santiago, Chile: IEEE.

Saqib, M., Khan, S. D., Sharma, N., & Blumenstein, M. (2018). Person Head Detection in Multiple Scales Using Deep Convolutional Neural Networks. International Joint Conference on Neural Networks (IJCNN) (pp. 10-15). Rio de Janeiro, Brazil: IEEE.

Wijaya, B. A., Husein, A. M., Harahap, M., & Harahap, M. K. (2017). Implementation Distance Transform Method in Kernel Discriminant Analysis for Face Recognition Using Kohonen SOM. International Journal of Engineering Research & Technology (IJERT), 28-31.

Most read articles by the same author(s)