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A traffic flow estimation method based on unsupervised change detection

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Abstract

With the rapid development of intelligent transportation, the video surveillance system as its important component has been achieved much attention. Traffic condition closely related to people’s lives needs to be tracked in time. Some methods estimate traffic flow by analyzing the pictures taken by fixed cameras. However, they can only estimate the traffic condition of particular roads. Different from the traditional traffic flow estimation methods, the proposed method explores the video information rather than traffic images acquired by sensing remote-sensing sensors in this letter. More specifically, the highlights of our work include the following parts: first, change detection is performed on analyzing the difference between one frame image extracted from Unmanned Aerial Vehicle (UAV) videos and an updated background image for the sake of recognizing the whole profile of every moving object. Second, a modified fuzzy c-means method is engaged in the process of change detection, which segments the road regions to enhance the profiles of moving objects and eliminate the noise of complex backgrounds. Finally, the estimation of traffic flow can be achieved by analyzing the change detection result. Besides, the videos shot by UAV on a crossroad are used to analyze the effectiveness of the proposed method. Experimental results on a series of binary images and proportion illustrations demonstrate the promising performance of the proposed method in terms of human visual perception and segmentation accuracy.

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References

  1. Liangyun, L., Shuyan, C., Tao, L.: Real-Time Traffic Estimation with Incomplete Information under Urban Traffic Network. In: 2017 International Conference on Smart City and Systems Engineering (ICSCSE), Changsha,  pp. 163–166 (2017)

  2. Shan, Z., Zhu, Q., Zhao, D.: Vehicle collision risk estimation based on rgb-d camera for urban road. Multimed. Syst. 23(1), 119–127 (2017)

    Article  Google Scholar 

  3. Mehboob, F., Abbas, M., Almotaeryi, R., Jiang, R., Al-Maadeed, S., Bouridane, A.: Traffic flow estimation from road surveillance. In: 2015 IEEE International Symposium on Multimedia (ISM), pp. 605–608. IEEE (2015)

  4. 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. Transport. Syst. 18(4), 890–901 (2017)

    Article  Google Scholar 

  5. Ke, R., Li, Z., Tang, J., Pan, Z., Wang, Y.: Real-time traffic flow parameter estimation from UAV video based on ensemble classifier and optical flow. IEEE Trans. Intell. Transport. Syst. 20(1), 54–64 (2019)

    Article  Google Scholar 

  6. Bui, K.H.N., Yi, H., Jung, H., Cho, J.: Video-based traffic flow analysis for turning volume estimation at signalized intersections. Intelligent Information and Database Systems, pp. 152–162. Springer International Publishing, Cham (2020)

    Chapter  Google Scholar 

  7. Sutarto, H.Y., Boel, R.K., Joelianto, E.: Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation. IET Control Theory Appl. 9(11), 1683–1691 (2015)

    Article  MathSciNet  Google Scholar 

  8. Pun, L., Zhao, P., Liu, X.: A multiple regression approach for traffic flow estimation. IEEE Access 7, 35998–36009 (2019)

    Article  Google Scholar 

  9. Cheng, A., Jiang, X., Li, Y., Zhang, C., Zhu, H.: Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Phys. A Stat. Mech. Appl. 466(C), 422–434 (2017)

    Article  Google Scholar 

  10. Cheng, H.Y.: Highway traffic flow estimation for surveillance scenes damaged by rain. IEEE Intell. Syst. 33(1), 64–77 (2018)

    Article  Google Scholar 

  11. Zheng, Z., Su, D.: Traffic state estimation through compressed sensing and Markov random field. Transport. Res. Part B Methodol. 91, 525–554 (2016)

    Article  Google Scholar 

  12. Zhu, G., Song, K., Zhang, P., Wang, L.: A traffic flow state transition model for urban road network based on hidden Markov model. Neurocomputing 214, 567–574 (2016)

    Article  Google Scholar 

  13. Levulis, S.J., Delucia, P.R., Kim, S.Y.: Effects of touch, voice, and multimodal input, and task load on multiple-UAV monitoring performance during simulated manned-unmanned teaming in a military helicopter. Hum. Factors 60(8), 1117–1129 (2018)

    Article  Google Scholar 

  14. Orfanus, D., De Freitas, E.P., Eliassen, F.: Self-organization as a supporting paradigm for military UAV relay networks. IEEE Commun. Lett. 20(4), 804–807 (2016)

    Article  Google Scholar 

  15. Zhang, X., Hao, X., Sun, G., Xu, Y.: Obstacle avoidance path planning of rotor UAV. In: China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume 1. CSNC 2017. Lecture Notes in Electrical Engineering, vol. 437. Springer, Singapore (2017)

  16. Karaduman, M., Çınar, A., Eren, H.: UAV traffic patrolling via road detection and tracking in anonymous aerial video frames. J. Intell. Robot. Syst. 95, 675–690 (2019)

    Article  Google Scholar 

  17. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  18. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., Berg, A.: Ssd: single shot multibox detector. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 9905 LNCS pp. 21–37 (2016)

  19. Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. J. Big Data 6(73), 1–15 (2019)

    Google Scholar 

  20. Kim, W.: Moving object detection using edges of residuals under varying illuminations. Multimed. Syst. 25(3), 155–163 (2019)

    Article  Google Scholar 

  21. Hu, X., Xu, X., Xiao, Y., Chen, H., He, S., Qin, J., Heng, P.: Sinet: a scale-insensitive convolutional neural network for fast vehicle detection. IEEE Trans. Intell. Transport. Syst. 20(3), 21–37 (2019)

    Article  Google Scholar 

  22. Wang, L., Lu, Y., Wang, H., Zheng, Y., Ye, H., Xue, X.: Evolving boxes for fast vehicle detection. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, pp. 1135–1140 (2017)

  23. Gong, M., Zhou, Z., Ma, J.: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process. 21(4), 2141–2151 (2012)

    Article  MathSciNet  Google Scholar 

  24. Avola, D., Cinque, L., Foresti, G.L., Martinel, N., Pannone, D., Piciarelli, C.: A UAV video dataset for mosaicking and change detection from low-altitude flights. IEEE Trans. Syst. Man Cybern. Syst. 50(6), 2139–2149 (2020)

    Article  Google Scholar 

  25. Krinidis, S., Chatzis, V.: A robust fuzzy local information c-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010)

    Article  MathSciNet  Google Scholar 

  26. Pei, H., Zheng, Z., Wang, C., Li, C., Shao, Y.: D-fcm: density based fuzzy c-means clustering algorithm with application in medical image segmentation. Procedia Comput. Sci. 122, 407–414 (2017)

    Article  Google Scholar 

  27. Lei, X., Ouyang, H.: Image segmentation algorithm based on improved fuzzy clustering. Clust. Comput. 62(1), 1–11 (2018)

    Google Scholar 

  28. Zhang, J., Zhou, Y., Xia, K., Jiang, Y., Liu, Y.: A novel automatic image segmentation method for Chinese literati paintings using multi-view fuzzy clustering technology. Multimed. Syst. 26(1), 37–51 (2020)

    Article  Google Scholar 

  29. Kulakarni, R., Chepuri, A., Arkatkar, S., Joshi, G.J.: Estimation of saturation flow at signalized intersections under heterogeneous traffic conditions. In: Transportation Research, pp. 591–605. Springer, Singapore (2020)

  30. Abbas, M., Mehboob, F., Khan, S.A., Rauf, A., Jiang, R.: Real time fuzzy based traffic flow estimation and analysis. Adv. Intell. Syst. Comput. 931, 472–482 (2019)

    Google Scholar 

  31. Hu, M.C., Cheng, W.H., Hu, C.S., Wu, J.L., Li, J.W.: Efficient human detection in crowded environment. Multimed. Syst. 21(2), 177–187 (2015)

    Article  Google Scholar 

  32. Ying, L., Zhang, T., Xu, C.: Multi-object tracking via mht with multiple information fusion in surveillance video. Multimed. Syst. 21(3), 313–326 (2015)

    Article  Google Scholar 

  33. Wang, X., Qi, W., Ghanbarikarekani, M.: Estimation of heavy vehicle passenger car equivalents for on-ramp adjacent zones under different traffic volumes: a case study. In: Intelligent Interactive Multimedia Systems and Services, pp. 338–346. Springer International Publishing (2019)

  34. Jiang, Y., Wen, X., Xiang, D., Tan, D., Li, Z., Zhang, S., Wan, Y.: A change detection approach of high-resolution imagery combined the pre-classification with the post-classification comparison. In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Tianjin, pp. 1–6 (2016)

  35. Wang, B., Choi, S., Byun, Y., Lee, S., Choi, J.: Object-based change detection of very high resolution satellite imagery using the cross-sharpening of multitemporal data. IEEE Geosci. Remote Sens. Lett. 12(5), 1151–1155 (2015)

    Article  Google Scholar 

  36. Ertürk, S.: Fuzzy fusion of change vector analysis and spectral angle mapper for hyperspectral change detection. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, pp. 5045–5048 (2018)

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Funding

This work was supported by National Natural Science Foundation of China (Grant no. 62076204), the National Natural Science Foundation of Shaanxi Province (Grant nos. 2018JQ6003 and 2018JQ6030), the China Postdoctoral Science Foundation (Grant nos. 2017M613204 and 2017M623246), the Fundamental Research Funds for the Central Universities, and the seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University.

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China

    Ying Zhou, Yu Lei, Shenghui Yang, Tao Shao, Dayong Tian & Jiao Shi

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  1. Ying Zhou
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  2. Yu Lei
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  3. Shenghui Yang
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  4. Tao Shao
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Correspondence to Yu Lei.

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Zhou, Y., Lei, Y., Yang, S. et al. A traffic flow estimation method based on unsupervised change detection. Multimedia Systems 27, 857–865 (2021). https://doi.org/10.1007/s00530-020-00721-1

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