Improved Multi-object Tracking Algorithm for Forward Looking Sonar Based on Rotation Estimation

  • Xiufen YeEmail author
  • Xinglong Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)


Multi-object tracking algorithm for forward-looking sonar (FLS) often requires carrier motion information, in order to correct the tracking error caused by the carrier motion. However, it is sometimes difficult to obtain carrier information or synchronize the sonar image with carrier attitude sensor data. Therefore, it is still meaningful to study tracking multiple objects without using carrier motion information. In this paper we focus on improving the performance of multi object tracking without navigation data when the sonar carrier is rotating. The traditional detection-by-tracking framework was improved by rotation estimation. A linear motion model in polar coordinate system was used, and both detection and data association were performed in polar coordinates system. Phase correction was used to estimate the rotation velocity. The velocity was added to the motion model as a control. The improved method was tested on real sonar sequence obtained in conditions which carrier rotated several times. For evaluating the algorithm, we presented a simple approach for object detection. Finally, the results of several tracking metrics show better performance compared with conventional tracking method.


Forward looking sonar Multi-object tracking Motion estimation 



This work was supported by the National Natural Science Foundation of China (Grant No. 41876100), the State Key Program of National Natural Science Foundation of China (Grant No. 61633004), the National key research and development program of China (Grant No. 2018YFC0310102 and 2017YFC0306002), the Development Project of Applied Technology in Harbin (Grant No. 2016RAXXJ071) and the Fundamental Research Funds for the Central Universities (Grant No. HEUCFP201707).


  1. 1.
    DeMarco, K.J., West, M.E., Howard, A.M.: Sonar-based detection and tracking of a diver for underwater human-robot interaction scenarios. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2378–2383, October 2013Google Scholar
  2. 2.
    Milan, A., Leal-Taixé, L., Reid, I.D., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking. CoRR abs/1603.00831 (2016),
  3. 3.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361, June 2012Google Scholar
  4. 4.
    Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6, August 2017Google Scholar
  5. 5.
    Karoui, I., Quidu, I., Legris, M.: Automatic sea-surface obstacle detection and tracking in forward-looking sonar image sequences. IEEE Trans. Geosci. Remote Sens. 53(8), 4661–4669 (2015)CrossRefGoogle Scholar
  6. 6.
    Kalyan, B., Balasuriya, A., Wijesoma, S.: Multiple target tracking in underwater sonar images using particle-PHD filter. In: OCEANS 2006 - Asia Pacific, pp. 1–5, May 2006Google Scholar
  7. 7.
    Quidu, I., Jaulin, L., Bertholom, A., Dupas, Y.: Robust multitarget tracking in forward-looking sonar image sequences using navigational data. IEEE J. Oceanic Eng. 37(3), 417–430 (2012)CrossRefGoogle Scholar
  8. 8.
    Hurtós, N., Cufí, X., Salvi, J.: Rotation estimation for two-dimensional forward-looking sonar mosaicing. Adv. Intell. Syst. Comput. 252, 69–84 (2014)Google Scholar
  9. 9.
    Machado, M., Zaffari, G., Ribeiro, P.O., Drews-Jr, P., Botelho, S.: Description and matching of acoustic images using a forward looking sonar: a topological approach. IFAC-PapersOnLine 50(1), 2317–2322 (2017). 20th IFAC World CongressCrossRefGoogle Scholar
  10. 10.
    Cardozo de Souza Ribeiro, P.O., Machado dos Santos, M., Lilles Jorge Drews, P., Silva da Costa Botelho, S.: Forward looking sonar scene matching using deep learning. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 574–579, December 2017Google Scholar
  11. 11.
    Cho, H., Pyo, J., Yu, S.: Drift error reduction based on the sonar image prediction and matching for underwater hovering. IEEE Sens. J. 16(23), 8566–8577 (2016)Google Scholar
  12. 12.
    Hamuda, E., Ginley, B.M., Glavin, M., Jones, E.: Improved image processing-based crop detection using kalman filtering and the hungarian algorithm. Comput. Electron. Agric. 148, 37–44 (2018). Scholar
  13. 13.
    Hurtós, N., Romagós, D., Cufí, X., Petillot, Y., Salvi, J.: Fourier-based registration for robust forward-looking sonar mosaicing in low-visibility underwater environ-ments. J. Field Robot. 32, 123–151 (2015)CrossRefGoogle Scholar
  14. 14.
    Kim, B., Cho, H., Yu, S.: Development of imaging sonar based autonomous trajectory backtracking using AUVs. In: 2016 IEEE/OES Autonomous Underwater Vehicles (AUV), pp. 319–323, November 2016Google Scholar
  15. 15.
    Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. 2008(1), 246309 (2008). Scholar
  16. 16.
    Ristani, E., Solera, F., Zou, R.S., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. CoRR abs/1609.01775 (2016). Scholar
  17. 17.
    Bradski, G.: The OpenCV Library. Dr. Dobb’s J. Softw. Tools 25, 120–125 (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Harbin Engineering UniversityHarbinChina

Personalised recommendations