Correlation filters (CFs) have been continuously advancing the state-of-the-art tracking performance and have been extensively studied in the recent few years. Most of the existing CF trackers adopt a cosine window to spatially reweight base image to alleviate boundary discontinuity. However, cosine window emphasizes more on the central region of base image and has the risk of contaminating negative training samples during model learning. On the other hand, spatial regularization deployed in many recent CF trackers plays a similar role as cosine window by enforcing spatial penalty on CF coefficients. Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization. When simply removing cosine window, CF with spatial regularization still suffers from small degree of boundary discontinuity. To tackle this issue, binary and Gaussian shaped mask functions are further introduced for eliminating boundary discontinuity while reweighting the estimation error of each training sample, and can be incorporated with multiple CF trackers with spatial regularization. In comparison to the counterparts with cosine window, our methods are effective in handling boundary discontinuity and sample contamination, thereby benefiting tracking performance. Extensive experiments on three benchmarks show that our methods perform favorably against the state-of-the-art trackers using either handcrafted or deep CNN features. The code is publicly available at this https URL.
Since the pioneering work of MOSSE @cite_20 , many improvements have been made to CF trackers. On the one hand, the CF models have been consistently improved with the introduction of non-linear kernel @cite_43 , scale adaptivity @cite_50 @cite_9 @cite_19 , long-term tracking @cite_47 , part-based CFs @cite_28 , particle filters @cite_16 , spatial regularization @cite_8 @cite_49 , continuous convolution @cite_26 @cite_24 , and formulation with multiple base images @cite_14 @cite_24 @cite_46 . On the other hand, progress in feature engineering, e.g., HOG @cite_4 , ColorName @cite_48 and deep CNN features @cite_1 @cite_37 @cite_33 , also greatly benefits the performance of CF trackers.
@cite_43 @cite_20 @cite_4 @cite_8 @cite_48 @cite_49 @cite_46 @cite_37 @cite_26 @cite_28 @cite_19 @cite_50 @cite_16 @cite_14 @cite_33 @cite_9 @cite_1 @cite_24 @cite_47
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For visual tracking, an ideal filter learned by the correlation filter (CF) method should take both discrimination and reliability information. However, existing attempts usually focus on the former one while pay less attention to reliability learning. This may make the learned filter be dominated by the unexpected salient regions on the feature map, thereby resulting in model degradation. To address this issue, we propose a novel CF-based optimization problem to jointly model the discrimination and reliability information. First, we treat the filter as the element-wise product of a base filter and a reliability term. The base filter is aimed to learn the discrimination information between the target and backgrounds, and the reliability term encourages the final filter to focus on more reliable regions. Second, we introduce a local response consistency regular term to emphasize equal contributions of different regions and avoid the tracker being dominated by unreliable regions. The proposed optimization problem can be solved using the alternating direction method and speeded up in the Fourier domain. We conduct extensive experiments on the OTB-2013, OTB-2015 and VOT-2016 datasets to evaluate the proposed tracker. Experimental results show that our tracker performs favorably against other state-of-the-art trackers. Over these years, Correlation Filter-based Trackers (CFTs) have aroused increasing interests in the field of visual object tracking, and have achieved extremely compelling results in different competitions and benchmarks. In this paper, our goal is to review the developments of CFTs with extensive experimental results. 11 trackers are surveyed in our work, based on which a general framework is summarized. Furthermore, we investigate different training schemes for correlation filters, and also discuss various effective improvements that have been made recently. Comprehensive experiments have been conducted to evaluate the effectiveness and efficiency of the surveyed CFTs, and comparisons have been made with other competing trackers. The experimental results have shown that state-of-art performance, in terms of robustness, speed and accuracy, can be achieved by several recent CFTs, such as MUSTer and SAMF. We find that further improvements for correlation filter-based tracking can be made on estimating scales, applying part-based tracking strategy and cooperating with long-term tracking methods. The aspect ratio variation frequently appears in visual tracking and has a severe influence on performance. Although many correlation filter (CF)-based trackers have also been suggested for scale adaptive tracking, few studies have been given to handle the aspect ratio variation for CF trackers. In this paper, we make the first attempt to address this issue by introducing a family of 1D boundary CFs to localize the left, right, top, and bottom boundaries in videos. This allows us cope with the aspect ratio variation flexibly during tracking. Specifically, we present a novel tracking model to integrate 1D Boundary and 2D Center CFs (IBCCF) where boundary and center filters are enforced by a near-orthogonality regularization term. To optimize our IBCCF model, we develop an alternating direction method of multipliers. Experiments on several datasets show that IBCCF can effectively handle aspect ratio variation, and achieves state-of-the-art performance in terms of accuracy and robustness. Learning a large-scale regression model has proven to be one of the most successful approaches for visual tracking as in recent correlation filter (CF)- based trackers. Different from the conventional CF-based algorithms in which the regression model is solved based on circulant training samples, we propose learning linear regression models via a single-convolutional layer with the gradient descent (GD) technique. In our convolution-based approach, the samples are cropped from an image in a sliding-window manner rather than being circularly shifted from one base sample. As a result, the abundant background context in the images can be fully exploited to learn a robust tracker. The proposed tracker is based on two independent regression models: a holistic regression model and a texture regression model. The holistic regression model is trained based on the entire object patch to predict the object location, whereas the texture regression model is trained based on the local object textures. The foreground map outputted by the texture regression model is not only helpful to boost the location prediction in the case of large variations, but is also an important clue for estimating the object size. With the foreground map outputted by the texture regression model, we are able to estimate the object size by optimizing a novel objective function based on object-background contrast. Our extensive experiments on four popular visual tracking datasets OTB-50, OTB-100, VOT-2016, and TempleColor have proved that the proposed algorithm achieves outstanding performance and outperforms most CF-based trackers.