Abstract
Recently, Correlation Filter (CF)-based methods have demonstrated excellent performance for visual object tracking. However, CF-based models often face one model degradation problem: With low learning rate, the tracking model cannot be updated as fast as the large-scale variation or deformation of fast motion targets; As for high learning rate, the tracking model is not robust enough against disturbance, such as occlusion. To enable the tracking model adapt with such variation effectively, a progressive updating mechanism is necessary. In order to exploit spatial and temporal information in original data for tracking model adaptation, we employ an implicit interpolation model. With motion-estimated interpolation using adjacent tracking frames, the obtained intermediate response map can fit the learning rate well, which will effectively alleviate the learning-related model degradation. The evaluations on the benchmark datasets KITTI and VOT2017 demonstrate that the proposed tracker outperforms the existing CF-based models, with advantages regarding the tracking accuracy.
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Acknowledgements
This work was partly funded by NSFC(NO.61571297), The National Key Research and Development Program (2017YFB1002401), and STCSM(18DZ2270700).
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Kong, X., Zhou, Q., Lai, Y., Zhao, M., Zhang, C. (2020). Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating. In: Singh, R., Vatsa, M., Patel, V., Ratha, N. (eds) Domain Adaptation for Visual Understanding. Springer, Cham. https://doi.org/10.1007/978-3-030-30671-7_9
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