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Channel Graph Regularized Correlation Filters for Visual Object Tracking

Correlation Filters (CF) are a popular choice for visual object tracking due to their efficiency in the frequency domain. Convolutional and hand-crafted features are jointly used when learning a filter, however, these features are not uniformly important when tracking a target. Given this observation, spatial and temporal regularization and attention models have been investigated. However, these models do not consider the interaction between different feature channels. As a result, dissimilar weights are assigned to similar feature channels. To address this issue, we propose a channel attention model and study two different regularization methods for attention. We investigate the application of channel regularization to emphasize important feature channels; and graph regularization which increases the likelihood of similar feature channels obtaining similar weights. The proposed formulation can be efficiently solved via the alternating direction method of multipliers. We first show the advantages of using the proposed channel regularization by demonstrating its performance when applied to two existing CF trackers. This is followed by analyzing the effect of using the proposed channel-graph regularization for CF based tracking. The evaluation is performed on publicly available tracking datasets: OTB100, TC128, VOT-2017, VOT-2019, LaSOT, UAV123, and GOT-10k. Evaluation over multiple challenges and a comparative analysis with existing top-ranked trackers shows that our formulation improves the discriminative power of the learned CF, preventing tracker drift during challenging scenarios.