Although the discriminative correlation filter‐ (DCF)‐based tracker improves tracking performance, some object representation issues can still be further optimized. On the one hand, the DCF tracker’s deep convolutional features contain many noisy channels, and assigning the same weights to multiple channels cannot distinguish the importance of different channels. On the other hand, a simple weighted fusion approach cannot fully utilize the benefits of different feature types. We propose a visual object tracking algorithm based on adaptive channel weighting and feature game fusion to solve these problems. In this study, an adaptive channel weighting strategy is designed to assign suitable weights to each channel based on the average energy ratio of the target and background regions in the feature channels and prune the channels with low weights to improve feature robustness and reduce computational complexity. Simultaneously, the game theory concept is introduced in the multifeature fusion. The handcrafted features are combined with shallow and deep convolutional features according to feature complementarity. Then, the two combined features are seen as two sides of the game, continuously gamed during the tracking process to generate a feature model with a higher representation capacity. Extensive experiments are conducted on four mainstream visual tracking benchmark datasets, including OTB2015, VOT2018, LaSOT, and UAV123. The experimental results show that the proposed algorithm performs outstandingly compared to the state‐of‐the‐art trackers.

Discriminative correlation filter (DCF)-based UAV tracking algorithms have drawn much attention due to their outstanding robustness and high computational efficiency. However, these algorithms are easily disturbed by background noise and abrupt changes in target appearance, leading to tracking failure. To address the issues above, we propose a real-time UAV object tracking algorithm with adaptive spatial-temporal attention. Specifically, we construct two filters with different roles based on the training sample's target foreground and environmental background. The spatial attention filter is implemented by incorporating a spatial context regularizer into the traditional DCF paradigm, which fully utilizes background environmental information to suppress background environmental noise and effectively distinguish between the target and the background. The temporal attention attention filter focuses on the continuity of the target samples, modeling only the target patch samples during the training process and introducing a temporal context regularizer, which substantially enhances the tracker's robustness against target occlusions and deformations. The two are jointly optimized by the Alternating Direction Method of Multipliers (ADMM) algorithm, which is mutually constrained during training and complemented during detection. Extensive experiments on three mainstream UAV benchmarks demonstrate the tracking advantages of the proposed algorithm.

The discriminative correlation filter (DCF) is commonly utilized in UAV tracking because of its high tracking accuracy and computing speed. However, in aerial tracking scenarios, challenges such as target occlusion and similar object interference are likely to cause the predicted object position to deviate from the correct motion trajectory. To alleviate this issue, this paper proposes a correlation filter algorithm based on trajectory correction and context interference suppression for real-time aerial tracking. First, a tracking quality evaluation metric is proposed to determine the confidence of the current tracking results. When the object is in a low confidence status, the state matrices of the object position and velocity are constructed, and the Kalman filter strategy is utilized to correct the tracking trajectory automatically. In addition, temporal context-response regularization is designed to fully exploit previous temporal information in order to suppress background interference. Extensive experimental results on four mainstream datasets demonstrate that the proposed algorithm has high tracking performance while achieving a real-time tracking speed of 32 fps on a single CPU.