The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, YOLOv2 (You Only Look Once—version 2) is used as an open source Convolutional Neural Network (CNN), to process high-resolution satellite images, in order to generate the spatio-temporal GIS (Geographic Information System) tracks of moving vehicles. At first step, YOLOv2 is trained with a set of images of 1024 × 1024 resolution from the VEDAI database. The model showed satisfactory results, with an accuracy of 91%, and then at second step, is used to process aerial images extracted from aerial video. The output vehicle bounding boxes have been processed and fed into the GIS based LinkTheDots algorithm, allowing vehicles identification and spatio-temporal tracks generation in GIS format.
References
[1]
Yoon, Y., Jeon, H.G., Yoo, D., Lee, J.Y. and So Kweon, I. (2015) Learning a Deep Convolutional Network for Light-Field Image Super-Resolution. Proceedings of the IEEE International Conference on Computer Vision Workshops, Santiago, 7-13 December 2015, 24-32. https://doi.org/10.1109/ICCVW.2015.17
[2]
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016) You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 779-788.
https://doi.org/10.1109/CVPR.2016.91
[3]
Cao, X., Jiang, X., Li, X. and Yan, P. (2016) Correlation-Based Tracking of Multiple Targets with Hierarchical Layered Structure. IEEE Transactions on Cybernetics, 48, 90-102. https://doi.org/10.1109/TCYB.2016.2625320
[4]
Chen, L. and Englund, C. (2015) Cooperative Intersection Management: A Survey. IEEE Transactions on Intelligent Transportation Systems, 17, 570-586.
https://doi.org/10.1109/TITS.2015.2471812
[5]
Ulmke, M. and Koch, W. (2006) Road-Map Assisted Ground Moving Target Tracking. IEEE Transactions on Aerospace and Electronic Systems, 42, 1264-1274.
https://doi.org/10.1109/TAES.2006.314571
[6]
Ibrahim, V.M. and Victor, A.A. (2012) Microcontroller Based Anti-Theft Security System Using GSM Networks with Text Message as Feedback. International Journal of Engineering Research and Development, 2, 18-22.
[7]
Hasberg, C., Hensel, S. and Stiller, C. (2011) Simultaneous Localization and Mapping for Path-Constrained Motion. IEEE Transactions on Intelligent Transportation Systems, 13, 541-552. https://doi.org/10.1109/TITS.2011.2177522
[8]
Almomani, I.M., Alkhalil, N.Y., Ahmad, E.M. and Jodeh, R.M. (2011) Ubiquitous GPS Vehicle Tracking and Management System. 2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Amman, 6-8 December 2011, 1-6. https://doi.org/10.1109/AEECT.2011.6132526
[9]
Maurya, K., Singh, M. and Jain, N. (2012) Real Time Vehicle Tracking System Using GSM and GPS Technology—An Anti-Theft Tracking System. International Journal of Electronics and Computer Science Engineering, 1, 1103.
[10]
Lee, S., Tewolde, G. and Kwon, J. (2014) Design and Implementation of Vehicle Tracking System Using GPS/GSM/GPRS Technology and Smartphone Application. 2014 IEEE World Forum on Internet of Things (WF-IoT), Seoul, 6-8 March 2014, 353-358. https://doi.org/10.1109/WF-IoT.2014.6803187
[11]
Pham, H.D., Drieberg, M. and Nguyen, C.C. (2013) Development of Vehicle Tracking System Using GPS and GSM Modem. 2013 IEEE Conference on Open Systems (ICOS), Kuching, 2-4 December 2013, 89-94.
https://doi.org/10.1109/ICOS.2013.6735054
[12]
Tang, Z., Naphade, M., Liu, M.Y., Yang, X., Birchfield, S., Wang, S., Hwang, J.N., et al. (2019) Cityflow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, 15-20 June 2019, 8797-8806.
https://doi.org/10.1109/CVPR.2019.00900
[13]
Tang, Z., Wang, G., Xiao, H., Zheng, A. and Hwang, J.N. (2018) Single-Camera and Inter-Camera Vehicle Tracking and 3D Speed Estimation Based on Fusion of Visual and Semantic Features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, 18-23 June 2018, 108-115.
https://doi.org/10.1109/CVPRW.2018.00022
[14]
Chen, Y., Jing, L., Vahdani, E., Zhang, L., He, M. and Tian, Y. (2019) Multi-Camera Vehicle Tracking and Re-Identification on AI City Challenge 2019. CVPR Workshops, Vol. 2, 324-332.
[15]
Hua, S., Kapoor, M. and Anastasiu, D.C. (2018) Vehicle Tracking and Speed Estimation from Traffic Videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, 18-23 June 2018, 153-160.
https://doi.org/10.1109/CVPRW.2018.00028
[16]
Peri, N., Khorramshahi, P., Rambhatla, S.S., Shenoy, V., Rawat, S., Chen, J.C. and Chellappa, R. (2020) Towards Real-Time Systems for Vehicle Re-Identification, Multi-Camera Tracking, and Anomaly Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, 14-19 June 2020, 622-623. https://doi.org/10.1109/CVPRW50498.2020.00319
[17]
Li, P., Li, G., Yan, Z., Li, Y., Lu, M., Xu, P., Chuxing, D., et al. (2019) Spatio-Temporal Consistency and Hierarchical Matching for Multi-Target Multi-Camera Vehicle Tracking. CVPR Workshops, Long Beach, 16-20 June 2019, 222-230.
[18]
Yang, D., Alsadoon, A., Prasad, P.C., Singh, A.K. and Elchouemi, A. (2018) An Emotion Recognition Model Based on Facial Recognition in Virtual Learning Environment. Procedia Computer Science, 125, 2-10.
https://doi.org/10.1016/j.procs.2017.12.003
[19]
Grigorescu, S., Trasnea, B., Cocias, T. and Macesanu, G. (2020) A Survey of Deep Learning Techniques for Autonomous Driving. Journal of Field Robotics, 37, 362-386. https://doi.org/10.1002/rob.21918
[20]
Loey, M., Manogaran, G., Taha, M.H.N. and Khalifa, N.E.M. (2021) Fighting against COVID-19: A Novel Deep Learning Model Based on YOLO-v2 with ResNet-50 for Medical Face Mask Detection. Sustainable Cities and Society, 65, Article ID: 102600. https://doi.org/10.1016/j.scs.2020.102600
[21]
Zou, Z., Shi, Z., Guo, Y. and Ye, J. (2019) Object Detection in 20 Years: A Survey.
[22]
Ardeshir, S., Zamir, A.R., Torroella, A. and Shah, M. (2014) GIS-Assisted Object Detection and Geospatial Localization. In: European Conference on Computer Vision, Springer, Cham, 602-617. https://doi.org/10.1007/978-3-319-10599-4_39
[23]
Campbell, A., Both, A. and Sun, Q.C. (2019) Detecting and Mapping Traffic Signs from Google Street View Images Using Deep Learning and GIS. Computers, Environment and Urban Systems, 77, Article ID: 101350.
https://doi.org/10.1016/j.compenvurbsys.2019.101350
[24]
Cheng, G. and Han, J. (2016) A Survey on Object Detection in Optical Remote Sensing Images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28.
https://doi.org/10.1016/j.isprsjprs.2016.03.014
[25]
Hurtik, P., Molek, V., Hula, J., Vajgl, M., Vlasanek, P. and Nejezchleba, T. (2020) Poly-YOLO: Higher Speed, More Precise Detection and Instance Segmentation for YOLOv3.
[26]
Bathija, A. and Sharma, G. (2019) Visual Object Detection and Tracking Using Yolo and Sort. International Journal of Engineering Research Technology, 8, 705-708.
[27]
Deng, L. and Yu, D. (2014) Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing, 7, 197-387. https://doi.org/10.1561/2000000039
[28]
Szegedy, C., Toshev, A. and Erhan, D. (2013) Deep Neural Networks for Object Detection.
[29]
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) Imagenet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
[30]
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. (2015) Imagenet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115, 211-252. https://doi.org/10.1007/s11263-015-0816-y
[31]
Yan, K., Wang, Y., Liang, D., Huang, T. and Tian, Y. (2016) CNN vs. Sift for Image Retrieval: Alternative or Complementary? Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, 15-19 October 2016, 407-411.
https://doi.org/10.1145/2964284.2967252
[32]
Fan, J., Ma, C. and Zhong, Y. (2019) A Selective Overview of Deep learning.
[33]
Ilin, R., Watson, T. and Kozma, R. (2017) Abstraction Hierarchy in Deep Learning Neural Networks. 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, 14-19 May 2017, 768-774.
https://doi.org/10.1109/IJCNN.2017.7965929
[34]
Schmidhuber, J. (2015) Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
[35]
Gülcehre, C. and Bengio, Y. (2016) Knowledge Matters: Importance of Prior Information for Optimization. The Journal of Machine Learning Research, 17, 226-257.
[36]
LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444.
https://doi.org/10.1038/nature14539
[37]
Deng, J., Berg, A., Satheesh, S., Su, H., Khosla, A. and Li, F.F. (2012) Large Scale Visual Recognition Challenge. https://image-net.org/challenges/LSVRC/2012/
[38]
Ucar, A., Demir, Y. and Güzelis, C. (2017) Object Recognition and Detection with Deep Learning for Autonomous Driving Applications. Simulation, 93, 759-769.
https://doi.org/10.1177/0037549717709932
[39]
Hubel, D.H. and Wiesel, T.N. (1968) Receptive Fields and Functional Architecture of Monkey Striate Cortex. The Journal of Physiology, 195, 215-243.
https://doi.org/10.1113/jphysiol.1968.sp008455
[40]
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W. and Jackel, L.D. (1989) Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1, 541-551. https://doi.org/10.1162/neco.1989.1.4.541
[41]
LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E. and Jackel, L.D. (1990) Handwritten Digit Recognition with a Back-Propagation Network. International Conference on Neural Information Processing Systems, Vol. 2, 396-404.
[42]
Chen, R.C. (2019) Automatic License Plate Recognition via Sliding-Window Darknet-YOLO Deep Learning. Image and Vision Computing, 87, 47-56.
https://doi.org/10.1016/j.imavis.2019.04.007
[43]
Bhanu, B., Ravishankar, C.V., Roy-Chowdhury, A.K., Aghajan, H. and Terzopoulos, D. (2011) Distributed Video Sensor Networks. Springer Science & Business Media, Berlin. https://doi.org/10.1007/978-0-85729-127-1
[44]
Berrigan, T. (2017) Busy Parking Lot—Aerial Time-Lapse.
[45]
Bengio, Y. (2009) Learning Deep Architectures for AI. Now Publishers Inc., Delft.
https://doi.org/10.1561/9781601982957
[46]
Redmon, J. and Farhadi, A. (2017) YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 21-26 July 2017, 7263-7271. https://doi.org/10.1109/CVPR.2017.690
