Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating

Xiyu Kong, Qiping Zhou, Yunyu Lai, Muming Zhao, Chongyang Zhang

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.