Abstract
Understanding the flow of traffic on road networks is increasingly important especially with the continued urbanization of the global population. Numerous hardware and software technologies have been applied to measure traffic volumes by Government agencies and/or organizations such as Google, however they are either expensive to deploy; limited in their ability to disambiguate the kinds of vehicles on the road network, or of increasing importance, they infringe on the privacy of individuals, e.g. tracking phones. In this paper we describe work applying deep learning technologies to identify and classify different vehicles on the road network of Victoria with specific focus on heavy goods vehicles (trucks and trailers). Specifically, we present an approach to automatically detect, classify and count the unique classes of trucks and trailers that are found on the road network and the direction of travel. We apply and compare leading deep learning approaches including You Only Look Once version 3 (YOLOv3) and Single Shot Multi-Box Detector (SSD). This paper builds upon earlier work [1] which focused on data (video) from a single traffic junction in Melbourne. This work is based on a wider range of data (videos) from locations reflecting the diversity of road use including multi-lane motorways, rural roads and city roads.
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Chen, L., Sun, P.-Y., Jia, Y., Sinnott, R.O.: Identification and classification of trucks and trailers on the road network through deep learning. In: BDCAT 2019 - Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (2019). https://doi.org/10.1145/3365109.3368781
Tzutalin: tzutalin/labelImg (2015). https://github.com/tzutalin/labelImg
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Huang, Z., Wang, J.: DC-SPP-YOLO: dense connection and spatial pyramid pooling based YOLO for object detection. arXiv preprint arXiv:1903.08589 (2019)
Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468 (2016)
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649 (2017)
Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960)
Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q. 2, 83–97 (1955)
Zheng, L., et al.: MARS: a video benchmark for large-scale person re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 868–884. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_52
Rajendran, S.P., Shine, L., Pradeep, R., Vijayaraghavan, S.: Real-time traffic sign recognition using YOLOv3 based detector. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7 (2019)
Valiati, G.R., Menotti, D.: Detecting pedestrians with YOLOv3 and semantic segmentation infusion. In: 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 95–100 (2019)
Pang, S., et al.: A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images. PloS One 14, e0217647 (2019)
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., Liang, Z.: Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Comput. Electron. Agric. 157, 417–426 (2019)
Choi, J., Chun, D., Kim, H., Lee, H.-J.: Gaussian yolov3: an accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 502–511 (2019)
Uchiyama, H., Marchand, E.: Object detection and pose tracking for augmented reality: recent approaches. Presented at the (2012)
Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34, 3–19 (2013)
Darms, M., Rybski, P., Urmson, C.: Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments. In: 2008 IEEE Intelligent Vehicles Symposium, pp. 1197–1202 (2008)
Kapania, S., Saini, D., Goyal, S., Thakur, N., Jain, R., Nagrath, P.: Multi object tracking with UAVs using deep SORT and YOLOv3 RetinaNet detection framework. In: Proceedings of the 1st ACM Workshop on Autonomous and Intelligent Mobile Systems. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3377283.3377284
Wang, Q., Cao, L., Xia, J., Zhang, Y., et al.: MTCNN-KCF-deepSort: driver face detection and tracking algorithm based on cascaded kernel correlation filtering and deep-SORT (2020)
Muller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: Trackingnet: a large-scale dataset and benchmark for object tracking in the wild. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 300–317 (2018)
Galoogahi, H.K., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: Need for speed: a benchmark for higher frame rate object tracking. arXiv preprint arXiv:1703.05884 (2017)
Wen, L., et al.: UA-DETRAC: a new benchmark and protocol for multi-object detection and tracking. Comput. Vis. Image Underst. 193, 102907 (2020)
Hou, X., Wang, Y., Chau, L.-P.: Vehicle tracking using deep SORT with low confidence track filtering. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2019)
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56
Fan, H., Ling, H.: Siamese cascaded region proposal networks for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7952–7961 (2019)
Zhang, Z., Peng, H.: Deeper and wider siamese networks for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4591–4600 (2019)
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Sun, PY., Sun, WY., Jin, Y., Sinnott, R.O. (2020). Heavy Vehicle Classification Through Deep Learning. In: Nepal, S., Cao, W., Nasridinov, A., Bhuiyan, M.Z.A., Guo, X., Zhang, LJ. (eds) Big Data – BigData 2020. BIGDATA 2020. Lecture Notes in Computer Science(), vol 12402. Springer, Cham. https://doi.org/10.1007/978-3-030-59612-5_16
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