Most existing correlation filter-based tracking algorithms only use the linear additive features or simple fusion features, which are unable to fully learn the appearance information of the target. In this article, a multi-expert and game theory correlation filter (MEGTCF) algorithm is proposed to treat the aforementioned problems. The algorithm extracts a variety of handcrafted features and deep features of the target and forms multiple experts in different combinations to track the target in parallel. In addition, the game theory strategy is introduced to obtain more reliable decisions. The expert evaluation mechanism evaluates and selects the most discriminative experts as game participants and fully explores the complementary information among different expert features through the iterative game, so as to realize the information interaction between expert features. We also propose a confidence indicator to calculate the weights of the experts with higher scores and adaptively update the expert model to avoid expert corruption. Extensive experiments are conducted on five popular benchmarks, including OTB2013, OTB2015, VOT2018, LaSOT, and the UAV123 tracking benchmark. The success rate and precision of the MEGTCF tracker on the OTB2015 reach 67.8% and 91.4%, respectively, which outperforms other state-of-the-art trackers based on correlation filters. In addition, the MEGTCF tracker is also highly suitable for challenging scenarios such as object deformation, background clutter, and occlusion.

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.