Correlation Filter Research Articles

Adaptive content-aware spatial regularized correlation filter tracking algorithm

In order to solve the annoying boundary effects in correlation filter (CF) trackers induced by cyclic shift when sampling training patches, and improve the tracking performance, an adaptive content aware spatially regularized correlation filter (ACSRCF) is proposed. Firstly, real negative samples are generated from the background area around the target object, so as to alleviate the filter degradation by the fake negative samples induced from the circularly shifted object patches. Secondly, the locality sensitive histogram (LSH) based foreground feature is extracted and incorporated with the spatial regularization weight which is updated adaptively according to the varied object-oriented appearances. Thereafter, the CF model is optimized using the alternative direction method of multipliers (ADMM) in which the model is decomposed into two sub-problems and the LSH-based features are used in iteration for obtaining the optimal solutions. Finally, the proposed method is evaluated on 5 public benchmarks. The experimental results show that the accuracy and success rate scores of our method on OTB 50 dataset are 90.3%and 66.1%, respectively, exceeding the other CF trackers .The data on the OTB100 dataset is 92.2%and 69.2%, and the accuracy first ranks among all the trackers, while the success rate is ahead of other CF trackers.

Real-Time Tracking Target System Based on Kernelized Correlation Filter in Complicated Areas.

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target's two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience.

An adaptive learning based aberrance repressed multi-feature integrated correlation filter for Visual Object Tracking (VOT)

Target tracking via Correlation Filter (CF) is a hot research area of computer vision domain, and offers various credible benefits. Existing CF algorithms face challenges when there are target appearance variations due to background noise, scale and illumination changes, occlusion, and fast motion, which severely degrades the overall tracker performance. To get maximum benefits, an object tracker should perform well with the less computational burden in the presence of real time challenging situations. To address this issue, a novel visual object trackeris proposed based on multi feature fusion and adaptive learning technique with aberrance suppression. At first, multiple features i.e., Histogram of gradient (HOG), Color Naming (CN), saliency, and gray level intensities are combined using feature fusion technique. Further, based on the evaluation of final fused response map using Peak-to-Sidelobe Ratio (PSR), an adaptive learning strategy is integrated to improve the learning phase of tracker. Tracking results show that the proposed strategy beats the other modern CF trackers with Distance Precision (DP) scores of 88.2%, 85.9%, and 74.1% and 64.7% over OTB2013, OTB2015, and TempleColor128 and UAV123 datasets respectively.

3D track extraction from a fluorescent nuclear track detector via machine learning and an application to diagnostics of laser-driven ions.

We have developed an ion diagnostic method for laser-driven ion acceleration experiments that uses fluorescent nuclear track detectors (FNTDs). An FNTD records the particle tracks as color centers and does not require chemical etching, unlike CR-39 track detectors. The color centers are observed using a confocal laser microscope, and 3D particle tracks can be obtained by changing its focal position. The intensity of the color centers corresponds to the energy deposited by the ions. The nuclides of the ions can be determined from the intensity distribution of the color centers as a function of depth and the distance between the stopping point and the surface of the detector. To extract the intensity distribution, we must track the same ion tracks in the depth-layered microscopic images from the surface to the stopping point, even if they overlap with those of other ions. In addition, since an FNTD is sensitive not only to ions but also to electrons and photons, we must identify ion tracks among those from the latter particles. To analyze a statistical number of ion tracks, it is necessary to automate these processes. We have thus developed a method for automated ion detection and 3D tracking that relies on a support vector classifier and a kernelized correlation filter. This method was tested on a laser ion acceleration experiment performed using the J-KAREN-P laser. The method automatically detects ion tracks on FNTDs and tracks them in the depth direction. The training data are sampled from the Heavy-Ion Medical Accelerator in Chiba.

Adaptive Aberrance Repressed Correlation Filters with Cooperative Optimization in High-Dimensional Unmanned Aerial Vehicle Task Allocation and Trajectory Planning

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.

Enhanced Filtrate Feature Selection Algorithm for Feature Subset Generation

Objectives: In the bioinformatics field feature selection plays a vital role in selecting relevant features for making better decisions and assessment of disease diagnosis. Brain Tumour (BT) is the second leading disease in the world. Most of the BT detection techniques are based on Magnetic Resonance (MR) images. Methods: In this paper, medical reports are used in the detection of BT to increase the surveillance of patients. To improve the accuracy of predictive models, a new adaptive technique called Enhanced Filtrate Feature Selection (EFFS) algorithm for optimal feature selection is proposed. Initially, the EFFS algorithm finds the dependency of each attribute and feature score by using Mutual Information Gain, Chi-Square, Correlation, and Fishore score filter methods. Afterward, the occurrence rate of each top-ranked attribute is filtered by applying threshold value and obtaining the optimal feature by using the Pareto principle. Findings: The performance of the selected optimal features is evaluated by time complexity, number of features selected, and accuracy. The efficiency of the proposed algorithm is measured and analyzed in a high-quality optimal subset based on a Random Forest classifier integrated with the ranking of attributes. The EFFS algorithm selects 39 out of 46 significant and relevant features with minimum selection time and shows 99.31 % of accuracy for BT, 29 features with 99.47% of accuracy for Breast Cancer, 15 features with 94.61% of accuracy for Lung Cancer, 15 features with 98.84% of accuracy for Diabetes and 43 features with 90% of accuracy for Covid-19 dataset. Novelty: To decrease the processing time and improve the performance of a model feature selection process will be done at the initial stages for the betterment of the classification task. Thus, the proposed EFFS algorithm is applied to different datasets based on medical reports and EFFS outperforms with greater performance measurements and time. The appropriate feature selection techniques help to diagnose diseases in the prior phase and increase the survival of human beings. Keywords: Bioinformatics, Brain Tumour, Chi­Square, Correlation, EFFS, Feature Selection, Fishore Score, Information Gain, Optimal Features, Random Forest

Research on the perception method of tiny objects in low-light and wide-field video

At present, many trackers exhibit commendable performance in well-illuminated scenarios but overlook target tracking in low-light environments. As night falls, the tracker's accuracy drops dramatically. Challenges such as high image resolution, intricate backgrounds, uneven illumination, and the resemblance between targets and backgrounds in Hawk-Eye surveillance videos make tracking small objects in low-light and wide-field scenarios exceedingly difficult for previous trackers. To address these challenges, this paper introduces an innovative approach by integrating the difference constraint method into the CF (correlation filters) tracker, which generates a change-aware mask using inter-frame difference information. In addition, a dual regression model and inter-frame difference constraint term are introduced to restrict each other for dual filter learning. In this paper, we construct a new benchmark comprising 41 night surveillance sequences captured by Hawk-Eye cameras. Exhaustive experiments are conducted on this benchmark. The results show that the proposed method maintains superior accuracy, surpasses state-of-the-art trackers in this dataset, and achieves a real-time performance of 27 fps on a single CPU, substantially advancing tiny object tracking on Hawk-Eye surveillance videos in low light and in night scenes.

Learning a Context-Aware Environmental Residual Correlation Filter via Deep Convolution Features for Visual Object Tracking

Visual tracking has become widespread in swarm robots for intelligent video surveillance, navigation, and autonomous vehicles due to the development of machine learning algorithms. Discriminative correlation filter (DCF)-based trackers have gained increasing attention owing to their efficiency. This study proposes “context-aware environmental residual correlation filter tracking via deep convolution features (CAERDCF)” to enhance the performance of the tracker under ambiguous environmental changes. The objective is to address the challenges posed by intensive environment variations that confound DCF-based trackers, resulting in undesirable tracking drift. We present a selective spatial regularizer in the DCF to suppress boundary effects and use the target’s context information to improve tracking performance. Specifically, a regularization term comprehends the environmental residual among video sequences, enhancing the filter’s discrimination and robustness in unpredictable tracking conditions. Additionally, we propose an efficient method for acquiring environmental data using the current observation without additional computation. A multi-feature integration method is also introduced to enhance the target’s presence by combining multiple metrics. We demonstrate the efficiency and feasibility of our proposed CAERDCF approach by comparing it with existing methods using the OTB2015, TempleColor128, UAV123, LASOT, and GOT10K benchmark datasets. Specifically, our method increased the precision score by 12.9% in OTB2015 and 16.1% in TempleColor128 compared to BACF.

A fast dual-module hybrid high-dimensional feature selection algorithm

When dealing with large-scale datasets, high-dimensional feature selection plays a crucial role in improving the performance and interpretability of machine learning models. However, it still faces the problems of the “dimensionality curse” and high computational cost in dealing with high-dimensional datasets. In this paper, we develop a dual-module hybrid feature selection algorithm based on correlation filtering and a multi-objective evolutionary algorithm based on dynamic variation distance (HMOFS-CFDVD), which aims to reduce the computational cost of high-dimensional features and search space. Firstly, a coarse-grained feature filtering method based on correlation is proposed, enabling the algorithm to quickly identify potentially better feature subsets in the later stages. After that, a fine-grained feature selection is further implemented based on the multi-objective evolutionary algorithm with sample variation distance to obtain a high-quality feature subset. This stage incorporates a novel population initialization method with self-regulation, an adaptive evolution strategy, an optimal sample selection mechanism based on dynamic sample distance, and an improved diversity mechanism to enhance the evolutionary performance. Moreover, the feature relevance metric is introduced as a third objective to improve the overall algorithm's performance. Experimental results on 12 high-dimensional feature datasets demonstrate that the proposed HMOFS-CFDVD achieves high accuracy and produces a smaller subset of features.

Correlation filter based single object tracking: A review

In recent years, correlation filter-based (CF) tracking algorithms have gained momentum in the field of visual tracking. CF tracking algorithms have achieved compelling performance by addressing its limitations such as boundary effect and filter corruption during various tracking and target appearance variations. Many researchers have attempted to provide better efficiency and tracking results by extracting handcrafted and deep features either from vision sensors or specialized sensors in the CF tracking framework. Handcrafted features are integrated with deep features, thermal features, and depth features to prevent tracking failures during dense tracking challenges. To provide a detailed understanding of CF-based trackers, the tracking algorithms are categorized either as kernelized CF trackers or fusion-based CF trackers in this work. This is the first review of its kind, which categorizes various correlation-based tracking algorithms based on the key methodologies and the exploited features in the appearance model. Under each category, salient features, detailed overview, and current advancement are discussed and tabulated to provide future research directions. In addition, the performance of CF-based state-of-the-art is experimentally evaluated on multiple datasets namely, OTB100 and VOT2017 under tough tracking situations.

Deep feature based correlation filter for single object tracking in satellite videos

Deep feature based correlation filter for single object tracking in satellite videos

Variational Online Learning Correlation Filter for Visual Tracking

Recently, discriminative correlation filters (DCF) have been successfully applied for visual tracking. However, traditional DCF trackers tend to separately solve boundary effect and temporal degradation problems in the tracking process. In this paper, a variational online learning correlation filter (VOLCF) is proposed for visual tracking to improve the robustness and accuracy of the tracking process. Unlike previous methods, which use only first-order temporal constraints, this approach leads to overfitting and filter degradation. First, beyond the standard filter training requirement, our proposed VOLCF method introduces a model confidence term, which leverages the temporal information of adjacent frames during filter training. Second, to ensure the consistency of the temporal and spatial characteristics of the video sequence, the model introduces Kullback–Leibler (KL) divergence to obtain the second-order information of the filter. In contrast to traditional target tracking models that rely solely on first-order feature information, this approach facilitates the acquisition of a generalized connection between the previous and current filters. As a result, it incorporates joint-regulated filter updating. Through quantitative and qualitative analyses of the experiment, it proves that the VOLCF model has excellent tracking performance.

Correlation-Filter-Based Channel and Feature Selection Framework for Hybrid EEG-fNIRS BCI Applications.

The proposed study is based on a feature and channel selection strategy that uses correlation filters for brain-computer interface (BCI) applications using electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) brain imaging modalities. The proposed approach fuses the complementary information of the two modalities to train the classifier. The channels most closely correlated with brain activity are extracted using a correlation-based connectivity matrix for fNIRS and EEG separately. Furthermore, the training vector is formed through the identification and fusion of the statistical features of both modalities (i.e., slope, skewness, maximum, skewness, mean, and kurtosis). The constructed fused feature vector is passed through various filters (including ReliefF, minimum redundancy maximum relevance, chi-square test, analysis of variance, and Kruskal-Wallis filters) to remove redundant information before training. Traditional classifiers such as neural networks, support-vector machines, linear discriminant analysis, and ensembles were used for the purpose of training and testing. A publicly available dataset with motor imagery information was used for validation of the proposed approach. Our findings indicate that the proposed correlation-filter-based channel and feature selection framework significantly enhances the classification accuracy of hybrid EEG-fNIRS. The ReliefF-based filter outperformed other filters with the ensemble classifier with a high accuracy of 94.77 ± 4.26%. The statistical analysis also validated the significance (p < 0.01) of the results. A comparison of the proposed framework with the prior findings was also presented. Our results show that the proposed approach can be used in future EEG-fNIRS-based hybrid BCI applications.

Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).

Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and duration of hospital stay in opioid poisoning, in order to facilitate appropriate clinical decision-making. This study used artificial intelligence technology to predict the maintenance dose and duration of administration by selecting clinical and paraclinical features that were selected by Pearson correlation (filter method) (Stage 1) and then the (wrapper method) Recursive Feature Elimination Cross-Validated (RFECV) (Stage2). The duration of administration was divided into two categories: A (which includes a duration of less than or equal to 24 h of infusion) and B (more than 24 h of naloxone infusion). XGBoost algorithm model with an accuracy rate of 91.04%, a prediction rate of 91.34%, and a sensitivity rate of 91.04% and area under the Curve (AUC) 0.97 was best model for classification patients. Also, the best maintenance dose of naloxone was obtained with XGBoost algorithm with R2 = 0.678. Based on the selected algorithm, the most important features for classifying patients for the duration of treatment were bicarbonate, respiration rate, physical sign, The partial pressure of carbon dioxide (PCO2), diastolic blood pressure, pulse rate, naloxone bolus dose, Blood Creatinine(Cr), Body temperature (T). The most important characteristics for determining the maintenance dose of naloxone were physical signs, bolus dose of 4.5 mg/kg, Glasgow Coma Scale (GCS), Creatine Phosphokinase (CPK) and intensive care unit (ICU) add. A predictive model can significantly enhance the decision-making and clinical care provided by emergency physicians in hospitals and medical settings. XGBoost was found to be the superior model.

Anti-Occlusion Visual Tracking Algorithm for UAVs with Multi-Feature Adaptive Fusion

Most of the existing trackers based on discriminative correlation filters use only one feature or a simple linear fusion of multiple features for object tracking, and most of them lack a mechanism to handle occlusions. This leads to poor tracking performance in rapidly changing and easily occluded scenarios, especially on unmanned aerial vehicle (UAV) platforms. To address this issue, this paper proposes an anti-occlusion visual tracking algorithm for UAVs with multi-feature adaptive fusion named multi-feature adaptive fusion and anti-occlusion tracker (MAFAOT). It introduces a novel approach for implementing an adaptive fusion of multiple features. This method transforms the multi-feature fusion problem into a maximization issue by designing a tracking quality evaluation index. It successfully achieves an adaptive fusion of gradient direction histogram and color histogram feature responses. MAFAOT also introduces an anti-occlusion update pool strategy, enabling the tracker to adapt dynamically to various complex scenarios, including occlusion and motion blur. The experimental results on the OTB100 and UAV123 datasets confirm the significant advantages of MAFAOT in terms of precision and success rate compared to other correlation filter-based algorithms. The proposed methods further enhance the expressiveness of the features and effectively avoid the problem of tracker contamination caused by occlusion. Furthermore, this paper applies the proposed methods to the kernelized correlation filters (KCF) algorithm. On the OTB100 dataset, the improved KCF algorithm shows an improvement of 10.94% in precision and 11.11% in success rate. On the UAV123 dataset, it shows an improvement of 14.53% in precision and 16.62% in success rate, further verifying the effectiveness and versatility of the proposed methods.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner’s reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

Adaptive target response-based spatio-temporal regularized correlation filter for UAV-based object tracking

Adaptive target response-based spatio-temporal regularized correlation filter for UAV-based object tracking

Correlation Filters for UAV Online Tracking Based on Complementary Appearance Model and Reversibility Reasoning

Correlation Filters for UAV Online Tracking Based on Complementary Appearance Model and Reversibility Reasoning

Cardiac Disease Diagnosis Using K-Nearest Neighbor Algorithm: A Study on Heart Failure Clinical Records Dataset

This article introduces an approach to diagnose heart diseases utilizing the K-Nearest Neighbor algorithm and diverse correlation filters for selecting the most pertinent attributes. Results high- light that meticulous filter selection enhances survival predictions in patients with heart diseases. Employing K = 5 and correlation filter CF = 0.1, key attributes for classification were identified as anemia, high blood pressure, serum creatinine, and sex. Omitting the 'time' attribute led to information loss but was crucial to prevent biases and generalize predictions across various clinical scenarios. Utilizing these classification parameters, we designed an Android mobile application called “Heart Info System”, functioning as an artificial intelligence service. It employs the K-Nearest Neighbor algorithm with optimal parameters to evaluate the probability of survival in the progression of heart disease. The main activity of the application retrieves data from a Firebase database. While the study results show promise, the accuracy of the application may be influenced by inaccurate or incomplete input data. Nevertheless, this application has the potential to improve the early detection of heart diseases, paving the way for life-saving interventions.

Zynq FPGA-Based Acceleration of Kernelized Correlation Filters via High-Level Synthesis of a Custom DFT Block

This study presents a hardware-software co-design implementation of an accelerator for the Kernelized Correlation Filter (KCF) tracking algorithm. Leveraging the High-level synthesis (HLS) and the Zynq heterogeneous platform, the KCF algorithm’s performance is enhanced by using a custom hardware implementation for the computationally intensive Discrete Fourier Transform (DFT) operation. Within this framework, a custom combined DFT and inverse DFT IP, named CDFT, is developed and optimized on the Programmable Logic (PL) side of the Xilinx ZCU102 FPGA, whereas the rest of the KCF algorithm is run with customized Petalinux build on the (Processing System) side. To assess real-world performance, a driver for the CDFT IP and a user application were created to measure metrics like Center Location Error (CLE), Intersection over Union (IoU), and Frame per Second (FPS). The designed DFT accelerator achieves a remarkable speedup of 21x compared to a software DFT implementation. At the algorithm level, the KCF accelerator obtains a 6x speed up with negligible precision loss. In comparison to prior studies employing exclusively hardware implementations, the proposed approach demonstrates a high accuracy at a moderate speed, while there exists potential for further optimizations to enhance its performance even further.