Correlation Filters For Visual Tracking Research Articles

Correction: A coupling method of learning structured support correlation filters for visual tracking

Correction: A coupling method of learning structured support correlation filters for visual tracking

Learning Target Point Seeking Weights Spatial–Temporal Regularized Correlation Filters for Visual Tracking

Learning Target Point Seeking Weights Spatial–Temporal Regularized Correlation Filters for Visual Tracking

A coupling method of learning structured support correlation filters for visual tracking

A coupling method of learning structured support correlation filters for visual tracking

Learning Adaptive Sparse Spatially-Regularized Correlation Filters for Visual Tracking

The correlation filter(CF)-based tracker is a classic and effective model in the field of visual tracking. For a long time, most CF-based trackers solved filters using only ridge regression equations with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2}$</tex-math></inline-formula> -norm, which can make the trained model noisy and not sparse. As a result, we propose a model of adaptive sparse spatially-regularized correlation filters (AS2RCF). Aiming to suppress the noise mixed in the model, we improve it by introducing an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -norm spatial regularization term. This converts the original ridge regression equation into an Elastic Net regression, which allows the filter to have a certain sparsity while maintaining the stability of model optimization. The entire AS2RCF model is optimized using alternating direction method of multipliers(ADMM), and quantitative evaluations through extensive experiments on OTB-2015, TC128 and UAV123 demonstrate the tracker's effectiveness.

Multi-layer Rotation Memory Model-based correlation filter for visual tracking

Object tracking technology is of great significance in laser image processing. However, occlusion or similar interference during visual object tracking may reduce the tracking precision or even cause tracking failure. Aiming at this issue, we propose a Multi-layer Rotation Memory Model-based Correlation Filter (MRMCF) for visual trackingin this paper. First, we establish a Multi-layer Rotation Memory (MRM) model, in which a set of three rotating concentric rings is used to simulate the three memory spacesand their updating processsimulate the memory spaces. Then we introduce the MRM model into the correlation filter tracking framework, which realizes realizing the dynamic updating of classifier parametersin the correlation filter. When the object is occluded or there is similar interference, the proposed tracker can use the Pre-occ classifier parameters stored in the memory spaces in the MRM model MRM memory spaces to retarget the object, thereby reducing the impact of these factors. The experimental results on the OTB50 dataset show that compared with trackers such as CNN-SVM, MEEM, Struck, etc., the proposed tracker achieves higher accuracy and success rate.

Learning background-aware and spatial-temporal regularized correlation filters for visual tracking

Learning background-aware and spatial-temporal regularized correlation filters for visual tracking

Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

With its outstanding performance and tracking speed, discriminative correlation filters (DCF) have gained much attention in visual object tracking, where time-consuming correlation operations can be efficiently computed utilizing the discrete Fourier transform (DFT) with symmetric properties. Nevertheless, the inherent issues of boundary effects and filter degradation, as well as occlusion and background clutter, degrade the tracking performance. In this work, we proposed an augmented memory joint aberrance repressed correlation filter (AMRCF) for visual tracking. Based on the background-aware correlation filter (BACF), we introduced adaptive spatial regularity to mitigate the boundary effect. Several historical views and the current view are exploited to train the model together as a way to reinforce the memory. Furthermore, aberrance repression regularization was introduced to suppress response anomalies due to occlusion and deformation, while adopting the dynamic updating strategy to reduce the impact of anomalies on the appearance model. Finally, extensive experimental results over four well-known tracking benchmarks indicate that the proposed AMRCF tracker achieved comparable tracking performance to most state-of-the-art (SOTA) trackers.

Spatial and long–short temporal attention correlation filters for visual tracking

Discriminative correlation filter is one of the quick and effective ways for studying visual tracking. However, discriminative correlation filter-based methods still suffer from many challenging questions caused by environmental interferences, such as spatial boundary effect, temporal filter degradation, and tracking drift. A novel appearance optimisation model, named spatial and long–short temporal attention model, has been proposed based on a new spatial regularisation term and a long–short temporal regularisation term for learning the correlation filter to localise the target. On the one hand, our proposed method can improve the classical spatial regularisation term with a new weight matrix to alleviate the spatial boundary effect. On the other hand, two new temporal regularisation terms are designed: a short temporal regularisation term and a long temporal regularisation term. The short temporal regularisation term can enlarge the inner connections of the current frame and all foregoing frames to improve the tracking performances, and the long temporal regularisation term can address the influence of occlusion by using the similarity between the initial filter and the current one. Extensive experiments on various benchmarks illustrate that our proposed tracker performs favourably against several related popular trackers.

Context and saliency aware correlation filter for visual tracking

Context and saliency aware correlation filter for visual tracking

Delayed rectification of discriminative correlation filters for visual tracking

Delayed rectification of discriminative correlation filters for visual tracking

Semantic‐aware spatial regularization correlation filter for visual tracking

Semantic‐aware spatial regularization correlation filter for visual tracking

SCSTCF: Spatial-Channel Selection and Temporal Regularized Correlation Filters for visual tracking

Recently, combining multiple features into discriminative correlation filters to improve tracking representation has shown great potential in object tracking. Existing trackers apply fixed weights to fuse features or fuse response maps, which cannot adapt to the object drift well. Moreover, in the tracking algorithm, using cyclic shift to obtain training samples always cause boundary effect, resulting in dissatisfied tracking effect. Therefore, we first design a multiple features fusion method. Various handcrafted features are fused with the same weight, then the fused handcrafted features and deep features are fused by adaptive weights, which considerably improves the representation ability of the tracking object. Second, we propose a correlation filter object function model called Spatial-Channel Selection and Temporal Regularized Correlation Filters. We perform the grouping features selection from the dimensions of channel, spatial and temporal, so as to establish the relevance between the multi-channel features and the correlation filter. Finally, we transform the objective function of the model with equality constraint to augmented Lagrangian multiplier formula without constraint, which is divided into three subproblems with closed-form solutions. The optimal solution is obtained by iteratively solving three subproblems using Alternating Direction Multiplier Method (ADMM). We conduct extensive experiments in four public datasets, OTB-2013, OTB-2015, TC128, UAV123, and VOT2016. The experimental results represent our proposed tracker performs favorably against other prevailing trackers in success rate and precision.

Accelerated duality-aware correlation filters for visual tracking

Accelerated duality-aware correlation filters for visual tracking

Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking

With the introduction of correlation filtering (CF), the performance of visual object tracking is significantly improved. Circular shifts collecting samples is a key component of the CF tracker, and it also causes negative boundary effects. Most trackers add spatial regularization to alleviate boundary effects well. However, these trackers ignore the effect of environmental changes on tracking performance, and the filter discriminates poorly in the background interference. Here, to break these limitations, we propose a new correlation filter model, namely Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking. Specifically, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames together to perceive environmental changes, which adjusts the learning rate to make the template more adaptable to environmental changes. To enhance the discriminatory capability of the filter, we use real background information as negative samples to train the filter model. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers.

Adaptive Multi-Feature Reliability Re-Determinative Correlation Filter for Visual Tracking

Model drift is a challenging issue for visual object tracking. Most available approaches aim to address this issue by constructing of a stronger discriminative prediction model with the integration of multiple features. In this article, we also address the issue of model drift using multiple features, but we consider the fusion problem from a different point of view, namely, strengthening the features that are suitable for the current scenario while weakening the remaining ones. Therefore, an advanced tracker that adaptively redetermines the reliability for each feature during the tracking process is proposed. Furthermore, to correctly evaluate and redetermine these reliabilities, two different solutions, called model evaluation and numerical optimization, are proposed, and two independent trackers corresponding to these two solutions are implemented. Extensive experiments have been designed on five large datasets to validate the following: 1) The proposed tracking framework is superior for making the tracking model more robust, and 2) the two solutions proposed for redetermining the reliability for each feature are effective. As expected, the two implemented trackers do indeed improve the accuracy and robustness compared to state-of-the-art trackers. Especially on VOT2016, the proposed trackers based on model evaluation and numerical optimization achieve outstanding EAO scores (i.e., <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.453</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.428</b> , respectively), outperforming the recently developed top trackers by a large margin.

Learning Target‐Adaptive Correlation Filters for Visual Tracking

AbstractCorrelation filters (CF) achieve excellent performance in visual tracking but suffer from undesired boundary effects. A significant amount of approaches focus on enlarging search regions to make up for this shortcoming. However, this introduces excessive background noises and misleads the filter into learning from the ambiguous information. In this paper, we propose a novel target‐adaptive correlation filter (TACF) that incorporates context and spatial‐temporal regularizations into the CF framework, thus learning a more robust appearance model in the case of large appearance variations. Besides, it can be effectively optimized via the alternating direction method of multipliers(ADMM), thus achieving a global optimal solution. Finally, an adaptive updating strategy is presented to discriminate the unreliable samples and alleviate the contamination of these training samples. Extensive evaluations on OTB‐2013, OTB‐2015, VOT‐2016, VOT‐2017 and TC‐128 datasets demonstrate that our TACF is very promising for various challenging scenarios compared with several state‐of‐the‐art trackers, with real‐time performance of 20 frames per second(fps).

Learning spatial-channel regularization jointly with correlation filter for visual tracking

The boundary effect of correlation filters is one key issue to limit the performance of visual tracking. Most existing methods focus on using regularization to constrain filters in the spatial domain, but less attention is paid to the channel information that is also important to enhance the discriminative ability of the filter. In this paper, we propose to learn the spatial-channel regularization jointly with the filter for visual tracking. Specifically, the channel regularization is integrated into the spatial regularization, which is exploited to make the filter more compact in spatial and channel domains. It benefits to improve the discriminative ability to identify the target from the background, even distractors. Additionally, the temporal coherence of the target is developed to enable the filter to be robust. The temporal regularization can suppress the sudden change of the spatial-channel regularization on the time axis, which can indirectly control the filter to pay attention to the target’s robust features. The regularization and filter can be jointly optimized by the alternating direction method of multipliers (ADMM) algorithm. We evaluate our method on a standard database. The results show that compared with traditional methods, our tracker has improved significantly in performance and can achieve real-time tracking.

Learning spatial-temporally regularized complementary kernelized correlation filters for visual tracking

Despite excellent performance shown by spatially regularized discriminative correlation filters (SRDCF) for visual tracking, some issues remain open that hinder further boosting their performance: first, SRDCF utilizes multiple training images to formulate its model, which makes it unable to exploit the circulant structure of the training samples in learning, leading to high computational burden; second, SRDCF is unable to efficiently exploit the powerfully discriminative nonlinear kernels, further negatively affecting its performance. In this paper, we present a novel spatial-temporally regularized complementary kernelized CFs (STRCKCF) based tracking approach. First, by introducing spatial-temporal regularization to the filter learning, the STRCKCF formulates its model with only one training image, which can not only facilitate exploiting the circulant structure in learning, but also reasonably approximate the SRDCF with multiple training images. Furthermore, by incorporating two types of kernels whose matrices are circulant, the STRCKCF is able to fully take advantage of the complementary traits of the color and HOG features to learn a robust target representation efficiently. Besides, our STRCKCF can be efficiently optimized via the alternating direction method of multipliers (ADMM). Extensive evaluations on OTB100 and VOT2016 visual tracking benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art trackers with a speed of 40 fps on a single CPU. Compared with SRDCF, STRCKCF provides a 8 × speedup and achieves a gain of 5.5% AUC score on OTB100 and 8.4% EAO score on VOT2016.

Hierarchical Spatiotemporal Context-Aware Correlation Filters for Visual Tracking

Discriminative correlation filters (DCF)-based trackers have been increasingly applied to visual tracking due to their high precision while running at high frame rates. However, most recent DCF-based methods solely concentrate on learning the correlation filter with spatial information and thus do not have sufficient descriptive power to discriminate the target from the background in the complex circumstances, such as full occlusion (OCC) and rapid target variation. In this article, we introduce a novel tracking framework that exploits the relationship between the target and its spatiotemporal context to improve tracking accuracy and robustness. Especially, we present our spatiotemporal context model in a hierarchical way, where each layer of the context pyramid is a spatial correlation filter learned from different temporal instances. For gaining an accurate spatiotemporal model, we propose an optimization fusion approach that can adaptively and efficiently learn the effect of each hierarchical layer and exploit these multiple temporal levels of correlation filters for visual tracking. Moreover, an adaptive model update strategy for correlation filters is introduced into the framework to dynamically select proper hierarchical layers, which boosts the temporal diversity of the target appearance, while radically reduces the number of model parameters and guarantees the real-time performance of the tracking method. The experimental results show that, with conventional handcrafted features, our tracker achieves the best success rates among available state-of-the-art trackers with handcrafted features, and provides state-of-the-art performance comparable to those of deep-learning-based trackers on OTB-2013, OTB-2015, VOT-2016, and UAV-20L benchmarks but runs significantly faster than deep trackers.

Learning reliable-spatial and spatial-variation regularization correlation filters for visual tracking

Single-object tracking is a significant and challenging computer vision problem. Recently, discriminative correlation filters (DCF) have shown excellent performance. But there is a theoretical defects that the boundary effect, caused by the periodic assumption of training samples, greatly limit the tracking performance. Spatially regularized DCF (SRDCF) introduces a spatial regularization to penalize the filter coefficients depending on their spatial location, which improves the tracking performance a lot. However, this simple regularization strategy implements unequal penalties for the target area filter coefficients, which makes the filter learn a distorted object appearance model. In this paper, a novel spatial regularization strategy is proposed, utilizing a reliability map to approximate the target area and to keep the penalty coefficients of relevant region consistent. Besides, we introduce a spatial variation regularization component that the second-order difference of the filter, which smooths changes of filter coefficients to prevent the filter over-fitting current frame. Furthermore, an efficient optimization algorithm called alternating direction method of multipliers (ADMM) is developed. Comprehensive experiments are performed on three benchmark datasets: OTB-2013, OTB-2015 and TempleColor-128, and our algorithm achieves a more favorable performance than several state-of-the-art methods. Compared with SRDCF, our approach obtains an absolute gain of 6.6% and 5.1% in mean distance precision on OTB-2013 and OTB-2015, respectively. Our approach runs in real-time on a CPU.