Spatial Adaptive Regularized Correlation Filter for Robust Visual Tracking
نویسندگان
چکیده
منابع مشابه
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to resolve this issue by enforcing spatial penalty on DCF coefficients, which, inevitably, improves the tracking performance at the price of increasing complexity. To tackle online updating, SRDCF formulates its model on multip...
متن کاملAttentional Correlation Filter Network for Adaptive Visual Tracking <Supplementary Material>
To show the effect of the parameters used in the Attentional Correlation Filter Network (ACFN), two additional experiments were conducted. In the first experiment, we varied the number of selected tracking modules (Na) in order to validate the robustness of the attentional mechanism, as shown in Fig. 2 (a). For this experiment, the number of tracking modules with high predicted validation score...
متن کاملFaster Spatially Regularized Correlation Filters for Visual Tracking
Discriminatively learned correlation filters (DCF) have been widely used in online visual tracking filed due to its simplicity and efficiency. These methods utilize a periodic assumption of the training samples to construct a circulant data matrix, which implicitly increases the training samples and reduces both storage and computational complexity.The periodic assumption also introduces unwant...
متن کاملStudent-tMixture Filter for Robust, Real-Time Visual Tracking
Filtering is a key problem in modern information theory; from a series of noisy measurement, one would like to estimate the state of some system. A number of solutions exist in the literature, such as the Kalman filter or the various particle and hybrid filters, but each has its drawbacks. In this paper, a filter is introduced based on a mixture of Student-t modes for all distributions, elimina...
متن کاملBiologically Inspired Particle Filter for Robust Visual Tracking
Although particle filter and its variants like KPF and UPF achieve great success in many visual tracking applications, they depend on local proposal distributions and hence always fall flat in cases of global object shift and large-scale object movement. To address this issue, we present the concept of global proposal distribution for particle filter with the inspiration from biological vision ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2964716