Real-time Target Tracking Research Articles

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.

Clinical implementation and patient-specific quality assurance solutions for real-time target tracking and dynamic delivery in Radixact synchrony.

The installation and testing of the first Radixact with Synchrony system in Colombia marked a significant milestone in Latin America's medical landscape. There was a need to devise a robust quality assurance protocol to comprehensively evaluate both dose delivery and motion tracking accuracy. However, testing experiences under clinical conditions have not been extensively reported. Additionally, there are limited recommended measuring devices for Synchrony evaluation. To validate and implement an alternative setup for dynamic-PSQA while testing Synchrony's functionality under clinical scenarios, including real-patient motion traces, and to provide guidance to new centers undergoing clinical implementation of Helical Synchrony. This approach involves using the Iba miniPhantomR with strategically placed fiducial markers for configuring Gafchromic-films and array-based setups. When paired with the CIRS Dynamic Platform, this enables an innovative dynamic setup with trackable features for Synchrony delivery testing. Assessment scenarios, including compensation (M1S1) and no-motion compensation (M1S0), were evaluated using 2D-gamma pass rate analysis with multiple clinical gamma criteria. The Synchrony-Simulation feature was used to assess pre-treatment performance and capture the patient's target motion pattern. Synchrony for common clinical cases with patient's motion-traces was validated. The results for M1S0 and M1S1 demonstrated consistency with previous studies evaluating Synchrony functionality. Analysis using different gamma criteria unveiled dosimetric differences and impacts across various motion ranges. The application of effective kV-dose subtraction for array-based methods is of upmost importance when evaluating dynamic-PSQA with stringent gamma-criteria. However, no significant kV-dose impact on EBT3-Film was detectable. Two implemented configurations for dynamic-PSQA setups were validated and successfully integrated into our clinic. We addressed both the benefits and limitations of array-based and film-based methods. The functionality and limitations of Synchrony were evaluated using the proposed setups. The potential utility of Synchrony-Simulation, along with the proposed patient-case classification table, can offer valuable support for new users during the clinical implementation of Synchrony treatments.

An Autonomous Intelligent Liability Determination Method for Minor Accidents Based on Collision Detection and Large Language Models

With the rapid increase in the number of vehicles on the road, minor traffic accidents have become more frequent, contributing significantly to traffic congestion and disruptions. Traditional methods for determining responsibility in such accidents often require human intervention, leading to delays and inefficiencies. This study proposed a fully intelligent method for liability determination in minor accidents, utilizing collision detection and large language models. The approach integrated advanced vehicle recognition using the YOLOv8 algorithm coupled with a minimum mean square error filter for real-time target tracking. Additionally, an improved global optical flow estimation algorithm and support vector machines were employed to accurately detect traffic accidents. Key frames from accident scenes were extracted and analyzed using the GPT4-Vision-Preview model to determine liability. Simulation experiments demonstrated that the proposed method accurately and efficiently detected vehicle collisions, rapidly determined liability, and generated detailed accident reports. The method achieved the fully automated AI processing of minor traffic accidents without manual intervention, ensuring both objectivity and fairness.

Harnessing the Power of Radiotherapy for Lung Cancer: A Narrative Review of the Evolving Role of Magnetic Resonance Imaging Guidance.

Compared with computed tomography (CT), magnetic resonance imaging (MRI) traditionally plays a very limited role in lung cancer management, although there is plenty of room for improvement in the current CT-based workflow, for example, in structures such as the brachial plexus and chest wall invasion, which are difficult to visualize with CT alone. Furthermore, in the treatment of high-risk tumors such as ultracentral lung cancer, treatment-associated toxicity currently still outweighs its benefits. The advent of MR-Linac, an MRI-guided radiotherapy (RT) that combines MRI with a linear accelerator, could potentially address these limitations. Compared with CT-based technologies, MR-Linac could offer superior soft tissue visualization, daily adaptive capability, real-time target tracking, and an early assessment of treatment response. Clinically, it could be especially advantageous in the treatment of central/ultracentral lung cancer, early-stage lung cancer, and locally advanced lung cancer. Increasing demands for stereotactic body radiotherapy (SBRT) for lung cancer have led to MR-Linac adoption in some cancer centers. In this review, a broad overview of the latest research on imaging-guided radiotherapy (IGRT) with MR-Linac for lung cancer management is provided, and development pertaining to artificial intelligence is also highlighted. New avenues of research are also discussed.

Improved Particle Filter Algorithm for Multi-Target Detection and Tracking.

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

Real-Time RGBT Target Tracking Based on Attention Mechanism

Real-Time RGBT Target Tracking Based on Attention Mechanism

A novel wall parameter estimation approach based on transmission line method from through-wall radar measurements

A novel technique for estimation of the wall’s relative permittivity, thickness and conductivity from signals measured from the through-wall radar is presented in this paper. The retrieval process is built around an electromagnetic circuit model that represents the wall structure as a transmission line segment. The propagation of the radar signal through the wall is equivalently described as a pair of voltage and current waves that propagate along the transmission line, using analogies between the electrical parameters of the transmission line segment and the constituent parameters of the materials of the wall structure. The synthetic time domain signal obtained analytically and the real through wall imaging time domain signal were correlated to estimate the wall parameters of the layers that comprise the wall structure. The effectiveness of the proposed estimation technique has been demonstrated through simulation and experimentation on two actual building walls. A comparison with conventional methods revealed consistent results between the estimated wall parameters using the proposed technique and those obtained through traditional approaches. These findings highlight the potential of the proposed method in enhancing the accuracy of real-time target localization and tracking in through-wall imaging applications.

Real-Time Object Detection and Tracking for Unmanned Aerial Vehicles Based on Convolutional Neural Networks

This paper presents a system applied to unmanned aerial vehicles based on Robot Operating Systems (ROSs). The study addresses the challenges of efficient object detection and real-time target tracking for unmanned aerial vehicles. The system utilizes a pruned YOLOv4 architecture for fast object detection and the SiamMask model for continuous target tracking. A Proportional Integral Derivative (PID) module adjusts the flight attitude, enabling stable target tracking automatically in indoor and outdoor environments. The contributions of this work include exploring the feasibility of pruning existing models systematically to construct a real-time detection and tracking system for drone control with very limited computational resources. Experiments validate the system’s feasibility, demonstrating efficient object detection, accurate target tracking, and effective attitude control. This ROS-based system contributes to advancing UAV technology in real-world environments.

Real-Time Target Tracking and Localization in Augmented Reality Scenes with Virtual Reality-Based Computer Vision Technology

Real-Time Target Tracking and Localization in Augmented Reality Scenes with Virtual Reality-Based Computer Vision Technology

AdapTrack: An adaptive FairMOT tracking method applicable to marine ship targets

Ship tracking at sea is faced with the disadvantages of complex sea conditions and the large influence of ship occlusion on the tracker. Therefore, we propose a method called AdapTrack based on On the Fairness of Detection and Re-Identification in Multiple Object Tracking (FairMOT) which is suitable for marine targets. The search strategy of trivial augmentation is used to randomly select suitable data augmentation methods and strengths. Then, based on the FairMOT tracking framework, we change the sampling selection method of positive and negative samples from a two-dimensional Gaussian distribution with the same variances to a two-dimensional Gaussian distribution with different variances. It is limited by the bounding box (bbox) of ground truth. This method can improve the detection algorithm’s fitness to the ship target. At the same time, we use Multi-Object Tracking by Associating Every Detection Box (ByteTrack)’s double-threshold strategy to divide detection bboxes, which improves the matching and inference speed. In the first stage of data association, the high-scoring bbox calculates the cost matrix of data association through the Re-identification (Re-ID) model. In the second stage, the Intersection over Union(IOU) cost matrix is calculated after merging low-scoring detection bboxes and unmatched detection bboxes of the first stage. The method achieves Multiple Object Tracking Accuracy (MOTA) of 36.1, Identification F-Score (IDF1) of 47.3, and Frames Per Second (FPS) of 28.79 on the Singapore-Marine-dataset. Experiments show that this method can better alleviate Identification Switch (ID-switch) and ensure the real-time tracking of complex and changeable ship target tracking at sea.

Improved Real Time Target Tracking System Based on Cam-shift and Kalman Filtering Techniques

This paper presents an improved real time target tracking system based on Cam-Shift and advanced Kalman filter algorithms. The paper, proposes a simple low implementation cost scheme while keeping the high tracking performance. Two degrees of freedom target tracking system based on two hybrid stepper motors with their drive systems is fully designed and implemented to improve the tracking accuracy while keeping low-cost. The potential of the proposed algorithm lies in its ability to accurately detect, track and produce firing commands on the designated target while utilizing low cost on the shelf embedded Microcontroller boards. A low cost Universal Serial Port (USB) camera is used and integrated with the proposed algorithm in order to detect and track maneuvering targets. A series of experiments for stationary and moving objects are carried out for testing the proposed system efficiency. The proposed system enables accurate performance for tracking static and moving objects. Hence it provides a simple and low implementation cost scheme for real time object tracking applications.

Evaluating motion of pancreatic tumors and anatomical surrogates using cine MRI in 0.35T MRgRT under free breathing conditions.

Treatment tolerability is a significant limitation to pancreatic cancer treatment with radiotherapy due to proximity to highly radiosensitive organs and respiratory motion necessitating expanded target margins. Further, pancreatic tumors are difficult to visualize on conventional radiotherapy systems. Surrogates are often used to locate the tumor but are often inconsistent and do not provide strong positional relations throughout the respiratory cycle. This work utilizes a retrospective dataset of 45 pancreatic cancer patients treated on an MR-Linac system with cine MRI acquired for real-time target tracking. We investigated intra-fraction motion of tumors and two abdominal surrogates, leading to prediction models between the tumor and surrogate. Patient specific motion evaluation and prediction models were generated from 225 cine MRI series acquired during treatment. Tumor contours were used to evaluate the pancreatic tumor motion. Linear regression and principal component analysis (PCA) based models were used to predict tumor position from the anterior-posterior (AP) motion of the abdominal surface, the superior-inferior (SI) motion of the diaphragm, or a combination. Models were evaluated using mean squared error (MSE) and mean absolute error (MAE). Contour analysis showed the average pancreatic tumor motion range was 7.4±2.7mm and 14.9±5.8mm in the AP and SI directions, respectively. The PCA model had MSE of 1.4mm2 and 0.6mm2 , for the SI and AP directions, respectively, with both surrogates as inputs for the models. When only the abdomen surrogate was used, MSE was 1.3mm2 and 0.4mm2 in the SI and AP directions, while it was 0.4mm2 and 1.3mm2 when only the diaphragm surrogate was used. We evaluated intra-fraction pancreatic tumor motion and demonstrated prediction models between the tumor and surrogate. The models calculated the pancreatic tumor position from diaphragm, abdominal, or both contours within standard pancreatic cancer target margin, and the process could be applied to other disease sites in the abdominothoracic cavity.

Real-Time Target Tracking System With Spiking Neural Networks Implemented on Neuromorphic Chips

Real-time target tracking is a usual task for humans despite the neural delays during the nervous system’s axonal transfer and neural processing. A plausible explanation is that the human brain employs predictive mechanisms to compensate for the delay. Inspired by the brain, this paper adopts a prediction network based on spiking neural networks (SNNs) to implement a real-time tracking task on a neuromorphic chip with low power consumption. The SNN-based prediction network outperforms the long short-term memory (LSTM) network on a small dataset and reduces 90% to 98% computations compared with LSTM. The quantized SNN-based network is deployed on a neuromorphic chip, and it takes 25ms and only 442 626nJ for a single prediction. The tracking performance of the system is also verified in real-life scenarios. Furthermore, the proposed real-time target tracking system can be easily ported to other neuromorphic platforms.

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion.

To improve the identification accuracy of target detection for intelligent vehicles, a real-time target detection system based on the multi-source fusion method is proposed. Based on the ROS melodic software development environment and the NVIDIA Xavier hardware development platform, this system integrates sensing devices such as millimeter-wave radar and camera, and it can realize functions such as real-time target detection and tracking. At first, the image data can be processed by the You Only Look Once v5 network, which can increase the speed and accuracy of identification; secondly, the millimeter-wave radar data are processed to provide a more accurate distance and velocity of the targets. Meanwhile, in order to improve the accuracy of the system, the sensor fusion method is used. The radar point cloud is projected onto the image, then through space-time synchronization, region of interest (ROI) identification, and data association, the target-tracking information is presented. At last, field tests of the system are conducted, the results of which indicate that the system has a more accurate recognition effect and scene adaptation ability in complex scenes.

Accelerated Particle Filter With GPU for Real-Time Ballistic Target Tracking

This study addresses the problem of real-time tracking of high-speed ballistic targets. Particle filters can be used to overcome the nonlinearity of motion and measurement models in ballistic targets. However, applying particle filters (PFs) to real-time systems is challenging since they generally require a significant computation time. So, most of the existing methods of accelerating PF using a graphics processing unit (GPU) for target tracking applications have accelerated computation weight function and resampling part. However, the computational time per part varies from application to application, and in this work, we confirm that it takes a lot of computational time in the model propagation part and propose accelerated PF by parallelizing the corresponding logic. The real-time performance of the proposed method was tested and analyzed using an embedded system. And compared to conventional PF on the central processing unit (CPU), the proposed method shows that the proposed method significantly reduces computational time by at least 10 times, improving real-time performance.

Parallelized Particle Filter With Efficient Pipelining on FPGA for Real-Time Ballistic Target Tracking

Parallelized Particle Filter With Efficient Pipelining on FPGA for Real-Time Ballistic Target Tracking

From target tracking to targeting track: A data-driven yet analytical approach to joint target detection and tracking

This paper addresses the problem of real-time detection and tracking of a non-cooperative target in the challenging scenario with almost no a-priori information about target birth, death, dynamics and detection probability. Furthermore, there are false and missing data at an unknown yet low rate in the measurements. The only information given in advance is about the target-measurement model and the constraint that there is no more than one target in the scenario. To solve these challenges, we model the movement of the target by using a polynomial trajectory function of time (T-FoT), which aims to estimate the continuous-time trajectory of the target rather than a series of discrete-time point estimates as is done in most existing filters/trackers. Data-driven T-FoT initiation and termination strategies are proposed for identifying the (re-)appearance and disappearance of the target. During the existence of the target, real target measurements are distinguished from clutter if the target indeed exists and is detected, in order to update the T-FoT at each scan for which we design a least-squares estimator. Overall, our approach is Markov-free, data-driven yet analytical. Simulations using either linear or nonlinear systems are conducted to demonstrate the effectiveness of our approach in comparison with the Bayes optimal Bernoulli filters. The results show that our approach is comparable to the perfectly-modeled filters, even outperforms them in some cases while requiring much less a-priori information and computing much faster.

Fast Deformable Image Registration for Real-Time Target Tracking During Radiation Therapy Using Cine MRI and Deep Learning

We developed a deep learning (DL) model for fast deformable image registration using 2-dimensional sagittal cine magnetic resonance imaging (MRI) acquired during radiation therapy and evaluated its potential for real-time target tracking compared with conventional image registration methods. Our DL model uses a pair of cine MRI images as input and provides a motion vector field (MVF) as output. The MVF is then applied to align the input images. A retrospective study was conducted to train and evaluate our model using cine MRI data from patients undergoing treatment for abdominal and thoracic tumors. For each treatment fraction, MR-linear accelerator delivery log files, tracking videos, and cine image files were analyzed. Individual MRI frames were temporally sampled to construct a large set of image registration pairs used to evaluate multiple methods. The DL model was optimized using 5-fold cross validation, and model outputs (transformed images and MVFs) using test set images were saved for comparison with 3 conventional registration methods (affine, b-spline, and demons). Evaluation metrics were 3-fold: (1) registration error, (2) MVF stability (both spatial and temporal), and (3) average computation time. We analyzed >21 hours of cine MRI (>629,000 frames) acquired during 86 treatment fractions from 21 patients. In a test set of 10,320 image registration pairs, DL registration outperformed conventional methods in both registration error (affine, b-spline, demons, DL; root mean square error: 0.067, 0.040, 0.036, 0.032; paired t test demons vs DL: t[20]=4.2, P < .001) and computation time per frame (51, 1150, 4583, 8 ms). Among deformable methods, spatial stability of resulting MVFs was comparable; however, the DL model had significantly improved temporal consistency. DL-based image registration can leverage large-scale MR cine data sets to outperform conventional registration methods and is a promising solution for real-time deformable motion estimation in radiation therapy.

Mutation detection dual correlation filter with an object-awareness module for real-time target tracking.

Discriminative correlation filter (DCF) based methods have recently been widely used for visual tracking tasks. The adaptive spatiotemporal-regulation based tracker (AutoTrack) can only partially solve some limitations of the DCF framework including filter degradation and the boundary effect, but its application scenarios need to be broadened, and performance improvements are also required. To further surmount these difficulties, this paper provides an object-awareness-module based mutation detection dual correlation filter (MDDCF-OAM). The main innovation points of this work are: (1) an object-mask based context enhancer is proposed to formulate a more robust appearance model; (2) a dual filter training-learning structure is adopted to allow the dual filters to restrict each other and suppress the filter degradation effect; (3) a Gaussian label map is updated with the refined joint response map to detect and attenuate the response mutation effects. Exhaustive experiments have been conducted to test the efficiency of the suggested MDDCF-OAM on four benchmarks, namely, OTB2015, UAV123, TC128, and VOT2019. The results indicate that: (1) the introduced MDDCF-OAM surpasses nine state-of-the-art trackers; (2) the MDDCF-OAM has a real-time speed of 32 frames per second, which is sufficient for target tracking tasks in numerous scenarios, especially unmanned aerial vehicles and camera tracking.

Identifying and Monitoring Students’ Classroom Learning Behavior Based on Multisource Information

Understanding human activity and behavior, particularly real-time understanding in video feeds, is one of the most active areas of research in Computer Vision (CV) and Artificial Intelligence (AI) nowadays. To advance the topic of integrating learning engagement research with university teaching practice, accurate and efficient assessment, and analysis of students’ classroom learning behavior engagement is very important. The recently proposed classroom behavior recognition algorithms have some limitations, such as the inability to quickly and accurately identify students’ classroom behaviors because they do not consider the motion information of students between consecutive frames. In recent years, action recognition algorithms based on Convolutional Neural Networks (CNN) have improved significantly. To address the limitations of existing algorithms, in this study, a 3D-CNN is selected as a network model for classroom student behavior recognition, which increases information multisourcing and classroom student localization with high accuracy and robustness. For better analysis of human behavior in videos, the 3D convolution extends the 2D convolution to the spatial–temporal domain. In the proposed system, first of all, a real-time picture stream of each student is obtained by combining real-time target detection and tracking. Then, a deep spatiotemporal residual CNN is used to learn the spatiotemporal features of each student’s behavior, so, as to achieve real-time recognition of classroom behaviors for multistudent targets in classroom teaching scenarios. To verify the effectiveness of the proposed model, different experiments are conducted using the labeled classroom behavior dataset. The experimental results demonstrate that the proposed model exhibits better performance in classroom behavior recognition. The accurate recognition of classroom behaviors can assist the teachers and students to understand the classroom learning situation and help to promote the development of smart classroom.