Discriminative correlation filters (DCFs) have shown excellent performance in visual tracking. DCF substitutes the sliding windows sampling strategy in traditional tracking methods with circular shift of the context area. Via projecting the filter learning into the frequency domain, DCF achieves satisfying performance and speed. Appropriate context area size has an influence on the performance of correlation filters. Small context area limits the CF’s ability to handle fast motion and partial occlusion, whereas large context area leads the CF to suffer from boundary effect. To make use of a large area of context and alleviate the accompanying drift risk, we propose a mask-constrained context correlation filter for object tracking. We first analyze the traditional window strategy via Taylor series and design a spatial mask that can be covered by a larger context area. Furthermore, the shape of the mask is adaptive to the target variation. Extensive experimental results in OTB-2015, VOT-2014, and VOT-2016 datasets demonstrate that this mask-constrained operation can improve the CF tracker performance in a large margin.
Discriminative correlation filters (DCFs) have shown excellent performance in visual tracking. DCF substitutes the sliding windows sampling strategy in traditional tracking methods with circular shift of the context area. Via projecting the filter learning into the frequency domain, DCF achieves satisfying performance and speed. Appropriate context area size has an influence on the performance of correlation filters. Small context area limits the CF’s ability to handle fast motion and partial occlusion, whereas large context area leads the CF to suffer from boundary effect. To make use of a large area of context and alleviate the accompanying drift risk, we propose a mask-constrained context correlation filter for object tracking. We first analyze the traditional window strategy via Taylor series and design a spatial mask that can be covered by a larger context area. Furthermore, the shape of the mask is adaptive to the target variation. Extensive experimental results in OTB-2015, VOT-2014, and VOT-2016 datasets demonstrate that this mask-constrained operation can improve the CF tracker performance in a large margin.
We propose a local feature representation based on two types of linear filtering, feature pooling, and nonlinear divisive normalization for remote sensing image classification. First, images are decomposed using a bank of log-Gabor and Gaussian derivative filters to obtain filtering responses that are robust to changes in various lighting conditions. Second, the filtering responses computed using the same filter at nearby locations are pooled together to enhance position invariance and compact representation. Third, divisive normalization with channel-wise strategy, in which each pooled feature is divided by a common factor plus the sum of the neighboring features to reduce dependencies among nearby locations, is introduced to extract divisive normalization features (DNFs). Power-law transformation and principal component analysis are applied to make DNF significantly distinguishable, followed by feature fusion to enhance local description. Finally, feature encoding is used to aggregate DNFs into a global representation. Experiments on 21-class land use and 19-class satellite scene datasets demonstrate the effectiveness of the channel-wise divisive normalization compared with standard normalization across channels and the fusion of the two types of linear filtering in improving classification accuracy. The experiments also illustrate that the proposed method is competitive with state-of-the-art approaches.
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