Abstract
Visual traffic counting so far has been carried out by static cameras at streets or aerial pictures from sky. This work initiates a new approach to count traffic flow by using populated vehicle driving recorders. Mainly vehicles are counted by a camera moves along a route on opposite lane. Vehicle detection is first implemented in video frames by using deep learning YOLO3, and then vehicle trajectories are counted in the spatial-temporal space called motion profile. Motion continuity, direction, and detection missing are considered to avoid multiple counting of oncoming vehicles. This method has been tested on naturalistic driving videos lasting for hours. The counted vehicle numbers can be interpolated as a flow of opposite lanes from a patrol vehicle for traffic control. The mobile counting of traffic is more flexible than the traffic monitoring by cameras at street corners.
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References
Shi, W., Kong, Q.-J., Liu, Y.: A GPS/GIS integrated system for urban traffic flow analysis. In: IEEE International Conference on Intelligent Transportation Systems, pp. 844–849 (2008)
Bagheri, S., Zheng, J.Y., Sinha, S.: Temporal mapping of surveillance video for indexing and summarization. Comput. Vis. Image Underst. 144, 237–257 (2016)
Ram, S., Rodriguez, J.: Vehicle detection in aerial images using multiscale structure enhancement and symmetry. In: International Conference on Image Processing (ICIP) (2016)
Ke, R., Li, Z., Kim, S., Ash, J., Cui, Z., Wang, Y.: Real-time bidirectional traffic flow parameter estimation from aerial videos. IEEE Trans. Intell. Transp. Syst. 18(4), 890–901 (2017)
Tian, R., Li, L., Yang, K., Chien, S., Chen, Y., Sherony, R.: Estimation of the vehicle-pedestrian encounter/conflict risk on the road based on TASI 110-car naturalistic driving data collection. In: IEEE IV 2014, pp. 623–629 (2014)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2017)
Redmon, J.: YOLO: real-time object detection (n.d.). https://pjreddie.com/darknet/yolo. Accessed 16 Apr 2019
Kilicarslan, M., Zheng, J.Y.: Visualizing driving video in temporal profile. In: 2014 IEEE Intelligent Vehicles Symposium, pp. 1263–1269 (2014)
Jazayeri, A., Cai, H., Zheng, J.Y., Tuceryan, M.: Vehicle detection and tracking in car video based on motion model. IEEE Trans. Intell. Transp. Syst. 12(2), 583–595 (2011)
Kilicarslan, M., Zheng, J.Y.: Predict vehicle collision by TTC from motion using a single video camera. IEEE Trans. Intell. Transp. Syst. 20, 522–533 (2019)
Gao, Z., Liu, Y., Zheng, J.Y., Yu, R., Wang, X., Sun, P.: Predicting hazardous driving events using multi-modal deep learning based on video motion profile and kinematics data. In: 21st International Conference on Intelligent Transportation Systems (ITSC) (2018)
Kilicarslan, M., Zheng, J.Y., Raptis, K.: Pedestrian detection from motion. In: International Conference on Pattern Recognition, pp. 1–6 (2016)
Kilicarslan, M., Zheng, J.Y., Algarni, A.: Pedestrian detection from non-smooth motion. In: IEEE Intelligent Vehicle Symposium, pp. 1–6 (2015)
Wheeler, T.A., Kochenderfer, M.J., Robbel, P.: Initial scene configurations for highway traffic propagation. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp. 279–284 (2015)
Sulistiyo, M.D., et. al.: Attribute-aware semantic segmentation of road scenes for understanding pedestrian orientations. In: IEEE International Conference on Intelligent Transportation Systems, pp. 1–6 (2018)
He, Z., Zheng, L., Song, L., Zhu, N.: A jam-absorption driving strategy for mitigating traffic oscillations. IEEE Trans. Intell. Transp. Syst. 18(4), 802–813 (2017)
Porikli, F., Li, X.: Traffic Congestion estimation using HMM models without vehicle tracking. In: IEEE Intelligent Vehicles Symposium (2004)
Cheng, G., Wang, Z., Zheng, J.Y.: Modeling weather and illuminations in driving views based on big-video mining. IEEE Trans. Intell. Veh. 3(4), 522–533 (2018)
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Kolcheck, K., Wang, Z., Xu, H., Zheng, J.Y. (2020). Visual Counting of Traffic Flow from a Car via Vehicle Detection and Motion Analysis. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_37
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DOI: https://doi.org/10.1007/978-3-030-41404-7_37
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