Abstract
Computer vision has received a significant attention in recent years, which is one of the important parts for robots to apperceive external environment. Discriminative Correlation Filter (DCF) based trackers gained more popularity due to their efficiency, however, most of the-state-of-the-art trackers are effective for short-term tracking, not yet successfully addressed in long-term scene. In this work, we tackle the problems by introducing Long-term Real-time Correlation Filter (LRCF) tracker. First, fused features only including HOG and Color Names are employed to boost the tracking efficiency. Second, we used the standard principal component analysis (PCA) to reduction scheme in the translation and scale estimation phase for accelerating. Third, we learned a long-term correlation filter to keep the long-term memory ability. Finally, we update the filter with interval updates. The extensive experiments on popular Object Tracking Benchmark OTB-2013 datasets have demonstrated that the proposed tracker outperforms the state-of-the-art trackers significantly achieves a high real-time (33FPS) performance in our mobile robot hardware. The experimental results show that the novel tracker performance is better than the-state-of-the-art trackers.
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Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)
Li, X., Hu, W., Shen, C., et al.: A survey of appearance models in visual object tracking. ACM Trans. Intell. Syst. Technol. (TIST) 4(4), 58 (2013)
Xu, K., Chia, K.W., Cheok, A.D.: Real-time camera tracking for marker-less and unprepared augmented reality environments. Image Vis. Comput. 26(5), 673–689 (2008)
Bolme, D.S., Beveridge, J.R., Draper, B.A., et al.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544–2550. IEEE (2010)
Henriques, J.F., Caseiro, R., Martins, P., et al.: High-speed tracking with Kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Lukezic, A., Vojir, T., Cehovin Zajc, L., et al.: Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6309–6318 (2017)
Kiani Galoogahi, H., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1135–1143 (2017)
Danelljan, M., Bhat, G., Shahbaz Khan, F., et al.: ECO: efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6638–6646 (2017)
Zhang, M., Liu, X., Xu, D., et al.: Vision-based target-following guider for mobile robot. IEEE Trans. Ind. Electron. PP(99), 1 (2019)
Bedaka, A.K., Vidal, J., Lin, C.Y.: Automatic robot path integration using three-dimensional vision and offline programming. Int. J. Adv. Manuf. Technol. 8, 1–16 (2019)
Wang, X.: Autonomous mobile robot visual SLAM based on improved CNN method. In: IOP Conference Series Materials Science and Engineering, vol. 466, p. 012114 (2018)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with Kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_50
Danelljan, M., Khan, F.S., Felsberg, M., et al.: Adaptive color attributes for real-time visual tracking. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2014)
Zhang, Z., Xie, Y., Xing, F., et al.: MDNet: a semantically and visually interpretable medical image diagnosis network (2017)
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
Valmadre, J., Bertinetto, L., Henriques, J., et al.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2805–2813 (2017)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)
Ma, C., Yang, X., Zhang, C., et al.: Long-term correlation tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396 (2015)
Ma, C., Huang, J.B., Yang, X.: Adaptive correlation filters with long-term and short-term memory for object tracking. Int. J. Comput. Vis. 126(8), 771–796 (2018)
Zhu, G., Wang, J., Wu, Y., et al.: Collaborative correlation tracking (2015)
Danelljan, M., Häger, G., Khan, F.S.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)
Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4021–4029 (2017)
Zhang, Y., Yang, Y., Zhou, W.: Motion-aware correlation filters for online visual tracking. Sensors 18(11), 3937 (2018)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)
Acknowledgments
This work has been supported by grant of the National Key Research and Development Program of China (No. 2018YFC0808000) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China.
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You, S., Zhu, H., Li, M., Wang, L., Tang, C. (2019). Long-Term Real-Time Correlation Filter Tracker for Mobile Robot. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11740. Springer, Cham. https://doi.org/10.1007/978-3-030-27526-6_22
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DOI: https://doi.org/10.1007/978-3-030-27526-6_22
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