### Object Tracking  
Recent developments of VOT have been witnessed to solve the VOT problem from the representation (e.g., correlation filter) based tracker [33; 34], discriminate and generative tracker [35], to deep Siamese networks based tracker [36; 37]. Conventional representation-based VOT methods rely on target appearance models based on handcrafted visual features and are prone to inaccuracies from background interference and occlusion. The discriminative and deep Siamese networks-based VOT methods are more robust but may fail to update adequately to sudden variations of the object in real-time applications due to high computation complexity. To achieve robust and real-time VOT, various recursive Bayesian filtering methods like the mean-shift (MS) tracker [38; 39], the Kalman filter (KF) tracker [40; 41; 42], and particle filter (PF) tracker [43; 44; 45] are commonly developed. Among the above, the KF tracker has become one of the most preferred tracking filters because of the high computational efficiency and robustness to missed detections or occlusions, especially for linear and Gaussian systems. The KF-based VOT method uses the background subtraction method for foreground object detection through background modeling and foreground segmentation and updates the object's state recursively by model prediction and measurement correlation. For the linear time-invariant (LTI) system model with the LTI measurement system, KF is proven to be the optimal linear minimum variance estimator [46]. However, in practice, the model mismatches and inaccurate noise statistics result in sub-optimal filtering that outputs unbiased and even divergent estimates [47]. The adaptive KF is one effective approach and has been applied in many applications [48; 49; 50]. In the KF-based object tracking task, the prior knowledge of the process and measurement noise covariance matrices is hard to determine due to occlusions and changes in object appearance. Therefore, designing adaptive KF focusing on noise covariance estimation is of vital importance. Existing noise covariance estimation methods typically utilize historical open-loop data, which can be categorized as Bayesian [51], maximum likelihood estimation (MLE) [52; 53], covariance matching (CM) [54; 55], and subspace identification (SI) [56], correlation [57; 58; 59; 60; 61; 62]. Each of these methods has its strengths and weaknesses. For instance, while the Bayesian method provides optimal posterior estimation using Bayes' rule to update the prior knowledge of the noise covariance, it suffers from computational complexity and sensitivity to hyper-parameter choices [51]. Similarly, while the MLE method maximizes the likelihood of innovations without prior knowledge, it is computationally intensive and sensitive to model misspecification [53]. The CM method, although robust and computationally efficient, is sensitive to outliers and unsuitable for online scenarios [63]. The correlation method, and especially the auto-covariance least-squares (ALS) method has attracted quite considerable attention as it provides an unbiased estimate based on the autocovariance of the innovations with acceptable computational complexity and no specific noise model requirements [60]. Therefore, the autocovariance least square estimation (ALS) method is integrated into the KF-based VOT method, and the AKF-ALS algorithm is proposed to address the requirements of simplicity and robustness in real-time visual object tracking under uncertain noise covariance. Our contributions are threefold:  
1. The proposed AKF-ALS algorithm provides a robust and efficient solution for noise covariance estimation in the visual object tracking problem. 2. A novel adaptive thresholding method based on the estimated process noise covariance that can predict sudden variations without heavy computations is proposed to improve the robustness of the BS method. 3.