Visual Tracking via Spatial-Temporal Regularized Correlation Filters with Advanced State Estimation
Visual Tracking via Spatial-Temporal Regularized Correlation Filters with Advanced State Estimation
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发表时间：
2020-12
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通讯作者：
Zhao-Qian Tang;K. Arakawa
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作者：
Zhao-Qian Tang;K. Arakawa
基于判别相关滤波器（CF）的视觉跟踪器利用手工特征在视觉跟踪中取得了出色的性能。在这项工作中，基于判别相关滤波器，我们提出了一种新的具有先进状态估计的时空正则化相关滤波器（CFASE），以实现更显著的跟踪性能。首先，考虑到跟踪过程中的漂移，我们提出了一种利用前两个滤波器的预测更精确地估计相关滤波器的新方法。其次，我们训练两个相关滤波器模型，分别用于获取尺度估计和目标位置。这两个分离的相关滤波器模型有助于减少尺度变化对目标位置的不利影响。第三，我们的跟踪器引入了平均峰值相关能量（APCE）来评估尺度估计和目标位置的准确性。在实验中，所提出的跟踪器（CFASE）在具有挑战性的基准序列（OTB2013、OTB2015和TC128）上取得了出色的实时性能。
Discriminative correlation filter (CF) based visual trackers achieves outstanding performance with the handcrafted feature in visual tracking. In this work, based on the discriminative correlation filter, we propose a new Spatial-Temporal regularized correlation filters with advanced state estimation (CFASE) to achieve more significant tracking performance. First, we propose a new method to estimate correlation filters more precisely using prediction from the previous two filters, considering the drift during the tracking process. Second, we train two correlation filters models to obtain scale estimation and object location, respectively. The separated two correlation filter models help to reduce the adverse effects of scale changes on object location. Third, our tracker introduces average peak-to-correlation energy (APCE) to evaluate the accuracy of scale estimation and object location. Experimentally, the proposed tracker (CFASE) achieves outstanding and real-time performance for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).