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Visual tracking is a very critical issue in computer vision and video processing. For Discriminative Correlation Filter (DCF)-based tracking methods, it is very essential and meaningful to adaptively incorporate reliable target and surrounding information from video frames. However, most existing DCF-based trackers solely rely on pre-defined and fixed constraints such as a binary mask or quadratic function-based regularization to improve the discrimination. Unfortunately, such attempts fail to adjust the constraints according to the change of tracking circumstance in the video sequence, and thus lead to the lack of reliability of learned filters. To mitigate these problems, we present a novel DCF-based tracking method that introduces an adaptive target-and-surrounding soft mask (ATSM) into the learning formula. The adaptive soft mask that is represented by float numbers contains the detail information for both target region and its surrounding information: first, for the background area, it introduces meaningful background information and suppressing uninformative one; second, for the target area inside the bounding box, it helps to focus on the reliable area and repress the rapidly changing area; third, the target-and-surrounding soft mask is adaptively adjusted based on the variations of the target and its surrounding during the tracking process. By jointly modeling the filter and the adaptive soft mask, our ATSM tracker achieves an efficient integration of meaningful information of both foreground and background and performs favorably against state-of-the-art algorithms on seven well-known benchmarks. [ABSTRACT FROM AUTHOR]

Detecting moving objects from the video sequences has been treated as a challenging computer vision task, since the problems of dynamic background, multi-scale moving objects and various noise interference impact the corresponding feasibility and efficiency. In this paper, a novel spatio-temporal online matrix factorization (STOMF) method is proposed to detect multi-scale moving objects under dynamic background. To accommodate a wide range of the real noise distractions, we apply a specific mixture of exponential power (MoEP) distributions to the framework of low-rank matrix factorization (LRMF). For the optimization of solution algorithm, a temporal difference motion prior (TDMP) model is proposed, which estimates the motion matrix and calculates the weight matrix. Moreover, a partial spatial motion information (PSMI) post-processing method is further designed to implement multi-scale objects extraction in varieties of complex dynamic scenes, which utilizes partial background and motion information. The superiority of the STOMF method is validated by massive experiments on practical datasets, as compared with state-of-the-art moving objects detection approaches. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental problems: 1) learning discriminative model parameters and 2) avoiding over-fitting, which often occurs in regression-based classification tasks. We formulate these two problems in terms of relaxing both the strict binary label matrix and graph regularization term into more flexible forms so that the margins between different classes are enlarged as much as possible and the problem of over-fitting is avoided to some extent. This task is accomplished by the proposed double relaxed regression (DRR) method. The convex problem of DRR is solved efficiently with an iterative procedure. Extensive experiments on synthetic and real world image data sets demonstrate the effectiveness of the proposed method in terms of both classification accuracy and running time. [ABSTRACT FROM AUTHOR]