Tracking Results Research Articles

Application Framework and Optimal Features for UAV-Based Earthquake-Induced Structural Displacement Monitoring

Unmanned aerial vehicle (UAV) vision-based sensing has become an emerging technology for structural health monitoring (SHM) and post-disaster damage assessment of civil infrastructure. This article proposes a framework for monitoring structural displacement under earthquakes by reprojecting image points obtained courtesy of UAV-captured videos to the 3-D world space based on the world-to-image point correspondences. To identify optimal features in the UAV imagery, geo-reference targets with various patterns were installed on a test building specimen, which was then subjected to earthquake shaking. A feature point tracking-based algorithm for square checkerboard patterns and a Hough Transform-based algorithm for concentric circular patterns are developed to ensure reliable detection and tracking of image features. Photogrammetry techniques are applied to reconstruct the 3-D world points and extract structural displacements. The proposed methodology is validated by monitoring the displacements of a full-scale 6-story mass timber building during a series of shake table tests. Reasonable accuracy is achieved in that the overall root-mean-square errors of the tracking results are at the millimeter level compared to ground truth measurements from analog sensors. Insights on optimal features for monitoring structural dynamic response are discussed based on statistical analysis of the error characteristics for the various reference target patterns used to track the structural displacements.

LocaLock: Enhancing Multi-Object Tracking in Satellite Videos via Local Feature Matching

Multi-object tracking (MOT) in satellite videos is a challenging task due to the small size and blurry features of objects, which often lead to intermittent detection and tracking instability. Many existing object detection and tracking models often struggle with these issues, as they are not designed to effectively handle the unique characteristics of satellite videos. To address these challenges, we propose LocaLock, a joint detection and tracking framework for MOT that incorporates feature matching concepts from single object tracking (SOT) to enhance tracking stability and reduce intermittent tracking results. Specifically, LocaLock utilizes an anchor-free detection backbone for efficiency and employs a local cost volume (LCV) module to perform precise feature matching in the local area. This provides valuable object priors to the detection head, enabling the model to “lock” onto objects with greater accuracy and mitigate the instability associated with small object detection. Additionally, the local computation within the LCV module ensures low computational complexity and memory usage. Furthermore, LocaLock incorporates a novel motion flow (MoF) module to accumulate and exploit temporal information, further enhancing feature robustness and consistency across frames. Rigorous evaluations on the VISO dataset demonstrate the superior performance of LocaLock, surpassing existing methods in tracking accuracy and precision within the demanding satellite video analysis domain. Notably, LocaLock achieved state-of-the-art performance on the VISO benchmark, achieving a multi-object tracking accuracy (MOTA) of 62.6 while ensuring fast running speed.

Computer Vision and Advanced Computational Algorithms for Risk Assessment and Performance Enhancement in Track and Field Teaching

This paper discusses the application of computer vision and advanced calculation algorithm in evaluating the teaching risk and teaching effect of track and field. Because of the inherent uncertainty and risk of PE and sports activities (especially track and field), it is necessary to establish an effective risk management mechanism. Using computer vision technology, this paper puts forward a method of analyzing and processing video data to detect and track moving objects, so as to identify potential risks in real time. This method not only improves the safety of students in track and field classes, but also provides valuable insights for improving teaching methods and reducing sports injuries. This paper discusses the background subtraction motion detection algorithm, which is very important for dynamic image modeling and shadow suppression, and can realize accurate motion state detection. The ultimate goal is to ensure the healthy development of school sports and optimize the teaching results of track and field sports.

Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise

Multi-object tracking (MOT) aims to detect objects in video sequences and associate them across frames. Currently, the mainstream research direction regarding MOT is the tracking-by-detection (TBD) framework. Tracking results are highly sensitive to detection outputs, and challenges from object occlusion and complex motion present significant obstacles in the field of MOT. To reduce dependence on detection outputs, we propose a method that integrates predictive information to improve Non-Maximum Suppression (NMS). By applying secondary modulation to the suppression scores and dynamically adjusting the suppression threshold using tracking information, our method better retains candidate boxes for occluded objects. Furthermore, to track occluding and overlapping objects more effectively, we introduce an adaptive measurement noise method that adjusts the measurement noise to mitigate the impact of object occlusion or overlap on tracking accuracy. Additionally, we enhance the affinity matrix in the association algorithm by incorporating height information, thereby improving the stability of complex moving objects. Our method outperforms the baseline model ByteTrack on the DanceTrack dataset, increasing Higher Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTA), and the ID F1 Score (IDF1) by 10.2%, 3.0%, and 4.8%, respectively.

Confidence-Guided Frame Skipping to Enhance Object Tracking Speed.

Object tracking is a challenging task in computer vision. While simple tracking methods offer fast speeds, they often fail to track targets. To address this issue, traditional methods typically rely on complex algorithms. This study presents a novel approach to enhance object tracking speed via confidence-guided frame skipping. The proposed method is strategically designed to complement existing methods. Initially, lightweight tracking is used to track a target. Only in scenarios where it fails to track is an existing, robust but complex algorithm used. The contribution of this study lies in the proposed confidence assessment of the lightweight tracking's results. The proposed method determines the need for intervention by the robust algorithm based on the predicted confidence level. This two-tiered approach significantly enhances tracking speed by leveraging the lightweight method for straightforward situations and the robust algorithm for challenging scenarios. Experimental results demonstrate the effectiveness of the proposed approach in enhancing tracking speed.

Isolation of Macrolactin A from a new Bacillus amyloliquefaciens and its aphicidal activity against Rhopalosiphum padi.

Rhopalosiphum padi is a major infection affecting cereal crops in the boundary area. However, continuous use of chemical pesticides has increased cases of drug resistance in its field population. Therefore, we aimed to verify the insecticidal properties of Bacillus amyloliquefaciens YJNbs21.10 against aphids, isolated to determine the bioactivity of its metabolite Macrolactin A against aphids for the first time. The results of activity tracking showed that the fermentation broth of YJNbs21.10 had the best inhibitory efficacy against R. padi, and the corrected efficiency reached 95.57% after 24 h. With the continuous separation and test, the efficiency of the active components decreased: Macrolactin A, as the most active substance, had a control activity against aphids under 500 mg L-1 of 74.64% at 72 h, (which was significantly lower than that of fermentation broth, indicating a synergistic effect between the active substances of each part of the strain. In addition, the result of the stereomicroscope showed that Macrolactin A damaged the body wall of aphids. The toxicity of Macrolactin A to R. padi was confirmed through the gradient test. In this study, Bacillus amyloliquefaciens YJNbs21.10 exhibited comparable inhibitory ability to chemical pesticides suggesting its potential to provide effective biological control on aphids. The biological activity of Macrolactin A against aphids was also verified for the first time, in this experiment, the EC50 of this substance against aphids was 169.02 mg L-1 (24 h), which provided strong evidence that YJNbs21.10 may act as an effective agent for the prevention of aphid. © 2024 Society of Chemical Industry.

Attention-Driven Memory Network for Online Visual Tracking.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

Long-term Target Tracking Based on Template Updating and Redetection

Abstract To address the issue of targets frequently disappearing and reappearing in long-term tracking scenarios due to occlusion and being out of view, we have developed a long-term target tracking algorithm based on template updating and redetection (LTUSiam). Firstly, on the basis of the basic tracker SiamRPN, a three-level cascade gated cycle unit is introduced to assess the state of the target and select the right time to adopt the template update network to adapt the update template information. Secondly, a re-detection algorithm based on template matching is proposed. The candidate region extraction module is utilized to adjust the target's position and size in the basic tracker, and the evaluation score sequence is used to judge the target loss to determine the tracking state of the next frame. Experiments show that LTUSiam achieves 28 frames per second on VOT2018_LT dataset, achieving good results in real-time tracking, and 0.644 performance on F-score, which has better robustness in handling the problem of target loss recurrence, and effectively improves the performance of long-term tracking.

Concurrent Learning-Based Adaptive Control of Underactuated Robotic Systems With Guaranteed Transient Performance for Both Actuated and Unactuated Motions.

With the wide applications of underactuated robotic systems, more complex tasks and higher safety demands are put forward. However, it is still an open issue to utilize "fewer" control inputs to satisfy control accuracy and transient performance with theoretical and practical guarantee, especially for unactuated variables. To this end, for underactuated robotic systems, this article designs an adaptive tracking controller to realize exponential convergence results, rather than only asymptotic stability or boundedness; meanwhile, unactuated states exponentially converge to a small enough bound, which is adjustable by control gains. The maximum motion ranges and convergence speed of all variables both exhibit satisfactory performance with higher safety and efficiency. Here, a data-driven concurrent learning (CL) method is proposed to compensate for unknown dynamics/disturbances and improve the estimate accuracy of parameters/weights, without the need for persistency of excitation or linear parametrization (LP) conditions. Then, a disturbance judgment mechanism is utilized to eliminate the detrimental impacts of external disturbances. As far as we know, for general underactuated systems with uncertainties/disturbances, it is the first time to theoretically and practically ensure transient performance and exponential convergence speed for unactuated states, and simultaneously obtain the exponential tracking result of actuated motions. Both theoretical analysis and hardware experiment results illustrate the effectiveness of the designed controller.

Research on High-Dynamic Tracking Algorithms for FH-BOC Signals

The rapid development of Low Earth Orbit (LEO) satellite navigation systems requires modulation schemes with strong anti-jamming capabilities, high spectral efficiency, and the ability to achieve precise tracking in high-dynamic environments. Traditional Binary Offset Carrier (BOC) modulation suffers from multi-peak ambiguity, leading to false lock issues. To address this, FH-BOC modulation, which integrates BOC modulationand frequency hopping, significantly improves both anti-jamming performance and spectral efficiency. Against this background, this paper proposes a comprehensive high-dynamic tracking algorithm for FH-BOC signals. (1) Based on the adaptive Kalman filter algorithm, high-precision carrier tracking was achieved in high-dynamic environments. (2) By leveraging the correlation between the ranging code and frequency-hopping offset carrier, a composite pseudo-code is generated through the XOR operation, and a corresponding composite code-tracking loop is introduced. (3) Based on code loop tracking results, the frequency-hopping moments of the subcarrier are detected, and a phase-locked loop for the frequency-hopping subcarrier is established. Simulation results indicate that the algorithm achieves centimeter-level pseudorange measurement accuracy for FH-BOC navigation signals under the JPL high-dynamic model.

Multi-Rotor Drone-Based Thermal Target Tracking with Track Segment Association for Search and Rescue Missions

Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed with an interacting multiple model (IMM) approach. During multiple-target tracking, tracks are initialized, maintained, and terminated. There are three different associations in track maintenance: measurement–track association, track–track association for tracks that exist at the same time (track association and fusion), and track–track association for tracks that exist at separate times (TSA). Measurement–track association selects the statistically nearest measurement and updates the track with the measurement through the IMM filter. Track association and fusion fuses redundant tracks for the same target that are spatially separated. TSA connects tracks that have become broken and separated over time. This process is accomplished through the selection of candidate track pairs, backward IMM filtering, association testing, and an assignment rule. In the experiments, a drone was equipped with an infrared thermal imaging camera, and two thermal videos were captured of three people in a non-visible environment. These three hikers were located close together and occluded by each other or other obstacles in the mountains. The drone was allowed to move arbitrarily. The tracking results were evaluated by the average total track life, average mean track life, and average track purity. The track segment association improved the average mean track life of each video by 99.8% and 250%, respectively

Intelligent vehicle path tracking coordinated optimization based on dual-steering cooperative game with fault-tolerant function

Since that a single controller for intelligent vehicle path tracking is difficult to adapt to complex working conditions and achieve good system control performance, inspired by the concept of control redundancy, an adaptive weight based dual-steering control cooperative game coordinated optimization with fault-tolerant control method for intelligent vehicle path tracking is proposed in this paper. Firstly, the three-degree-of-freedom of lateral, yaw and longitudinal directions is considered to establish the vehicle single-track dynamics model, and two model predictive path tracking steering controllers with different control objective emphases in tracking accuracy and yaw stability are designed. Then, the cooperative game theory is introduced to model the steering interaction of dual controllers, and its Pareto equilibrium optimal solution is solved by the distributed model predictive control framework. According to the influence trend of two game players’ relative participation weights on the path tracking results, an adaptive weight adjustment strategy based on predicted output deviation is designed, to achieve the coordinated optimization of dual steering control, and the steering fault-tolerant control is also realized by cooperative game in some cases of steering partial failure. Carsim/Simulink co-simulation and hardware-in-loop test under the variable curvature path and control failure conditions prove that by the proposed method intelligent vehicle path tracking dual steering control can be coordinated optimized and optimal path tracking performance is obtained, and demonstrate the effectiveness of the adaptive weight adjustment method which has better fault-tolerant control compared to fixed weight allocation under different degrees of steering output failures.

Depth gate tracking method based on historical sounding results in MBES

Gate tracking technology plays a crucial role in the autonomous detection of multi-beam echo sounding. The quality of the tracking algorithm directly impacts the quality of the estimate data. Here, we propose a new method for depth gate tracking in multi-beam echo sounding based on the assumption that historical sounding results are geometrically similar to the current ping results. Considering Hausdorff distance measures how far two subsets of a metric space are from each other. An evaluation method based on the concept of similarity distance that relies on the Modified Hausdorff distance is proposed here to measure the similarity between the detection results of different pings and determine the maximum number of historical pings that can be tracked. Then, this model is combined with the iterative closest point registration algorithm to align the bathymetric results of historical pings to the current ping, eliminating the need for prior registration. The registered data are used to initialize the particle distribution in the tracking particle filter and a distance-weighted importance function is established for each beam. Validation on measured data has shown that the proposed method is effective in tracking seabed topography and providing stable gate tracking results.

SlicerCineTrack: An open-source research toolkit for target tracking verification in 3D Slicer

Purpose:Target motion monitoring plays a significant role in several computer-assisted interventions. However, ensuring the reliability of tracking algorithms can be challenging without adequate tools. We introduce SlicerCineTrack, a free open-source research toolkit, designed to provide users with a user-friendly interface for visualizing their target tracking results. Methods:SlicerCineTrack was developed as an extension to 3D Slicer. It enables users to visualize target tracking results by sequentially playing back cine medical images, and simultaneously overlaying the target segmentation at the locations indicated by the tracking results. The extension was evaluated by established experts in computer-assisted interventions and image-guided therapy. Results:SlicerCineTrack is available for download from the 3D Slicer extension catalog for stable releases, and its GitHub repository for preview releases. Evaluation results demonstrate SlicerCineTrack’s effectiveness in discriminating between different tracking performances. Moreover, the experts found the extension convenient to use due to its intuitive and user-friendly interface. Conclusions:SlicerCineTrack was found to be effective at verifying the reliability of tracking algorithms. In turn, SlicerCineTrack shows potential for target tracking verification, as well as algorithm validation and refining through parameter tuning.

CF-SOLT: Real-time and accurate traffic accident detection using correlation filter-based tracking

Traffic accident detection using video surveillance is valuable research work in intelligent transportation systems. It is useful for responding to traffic accidents promptly that can avoid traffic jam or prevent secondary accident. In traffic accident detection, tracking occluded vehicles in real-time and accurately is one of the major sticking points for practical applications. In order to improve the tracking of occluded vehicles for traffic accident detection, this paper proposes a simple online tracking scheme with correlation filters (CF-SOLT). The CF-SOLT method utilizes a correlation filter-based auxiliary tracker to assist the main tracker. This auxiliary tracker helps prevent target ID switching caused by occlusion, enabling accurate vehicle tracking in occluded scenes. Based on the tracking results, a precise traffic accident detection algorithm is developed by integrating behavior analysis of both vehicles and pedestrians. The improved accident detection algorithm with the correlation filter-based auxiliary tracker can provide shorter response time, enabling quick identification and detection of traffic accidents. The experiments are conducted on the VisDrone2019, MOT-Traffic and Dataset of accident to evaluate the performances metrics of MOTA, IDF1, FPS, precision, response time and others. The results show that CF-SOLT improves MOTA and IDF1 by 5.3% and 6.7%, accident detection precision by 25%, and reduces response time by 56 s.

Efficient transformer tracking with adaptive attention

AbstractRecently, several trackers utilising Transformer architecture have shown significant performance improvement. However, the high computational cost of multi‐head attention, a core component in the Transformer, has limited real‐time running speed, which is crucial for tracking tasks. Additionally, the global mechanism of multi‐head attention makes it susceptible to distractors with similar semantic information to the target. To address these issues, the authors propose a novel adaptive attention that enhances features through the spatial sparse attention mechanism with less than 1/4 of the computational complexity of multi‐head attention. Our adaptive attention sets a perception range around each element in the feature map based on the target scale in the previous tracking result and adaptively searches for the information of interest. This allows the module to focus on the target region rather than background distractors. Based on adaptive attention, the authors build an efficient transformer tracking framework. It can perform deep interaction between search and template features to activate target information and aggregate multi‐level interaction features to enhance the representation ability. The evaluation results on seven benchmarks show that the authors’ tracker achieves outstanding performance with a speed of 43 fps and significant advantages in hard circumstances.

The Effect of Low-Dose CT Protocols on Shoulder Model-Based Tracking accuracy Using Biplane Videoradiography.

Model-based tracking is being increasingly used to quantify shoulder kinematics and typically employs computed tomography (CT) to create the 3D bone volumes, which adds to the total radiation exposure. Lower-dose CT protocols may be possible given the contrast between bone and the surrounding soft tissues. The purpose of this study was to describe the dose-accuracy tradeoff between low-dose CT scans and the kinematic tracking accuracy of the humerus, scapula, and clavicle when tracked using an intensity-based registration algorithm. Three fresh-frozen cadavers consisting of the torso and bilateral shoulders were tested. The CT protocols investigated included a full-dose protocol and 4 experimental low-dose protocols that modulated x-raytube current and peak voltage. Bead-based tracking (i.e., radiostereometric analysis) served as the reference standard to which model-based tracking results were compared. Accuracy was described in terms of both segmental (humerus, scapula, and clavicle) and joint (glenohumeral, acromioclavicular) kinematics using root-mean-square (RMSE), bias, precision, and worst-case errors. The low-dose CT scans resulted in an average dose reduction of 70.6-92.8%. RMSEs tended to increase as CT dose decreased with average glenohumeral errors increasing from 0.5° and 0.6 mm to 0.6° and 0.6 mm between the highest and lowest-dose protocols, and average acromioclavicular errors increasing from 0.6° and 0.8 mm to 0.7° and 0.9 mm. However, the difference in joint kinematic errors between the highest and lowest-dose CT scanning protocols was generally small (≤0.3°, ≤ 0.1 mm). It is possible to substantially reduce the CT dose associated with shoulder motion analysis using biplane videoradiography without significantly impacting data fidelity.

Truncated predictive control for delayed cyber–physical systems under deception attacks

In this work, the state tracking, input delay prediction, uncertainty and disturbance rejection problems are studied for the delayed cyber–physical systems under uncertainties, nonlinearities and external disturbances in which the addressed model is described by the fuzzy technique. More specifically, such an input-delayed system is linearized through an interval type-2 fuzzy technique accompanied by a parallel distribution compensation method. Further, the presence of constant input delays in the examined system is proficiently compensated with the assistance of a truncated predictor feedback approach. In parallel, the impacts of external disturbances and uncertainties that are occurred in the system are estimated by means of uncertainty and disturbance estimator method in an efficacious way. With the help of this estimated information, an uncertainty and disturbance estimator-based truncated predictive tracking control protocol is formulated to acquire the state tracking results of the underlying system in the presence of external disturbances and uncertainties. Remarkably, in this study, the interval type-2 fuzzy is blended with the designed reference system. Furthermore, the secure tracking control law is designed for the considered system under the random deception attacks. To address the deception attacks, the Bernoulli stochastic distribution is considered in this study. Moreover, the constraints are expressed for computing the truncated predictive feedback control gain matrices. Conclusively, the numerical simulation analysis is carried out to demonstrate the usefulness of the devised approach.

First test beam of the DMAPS-based proton tracker at the pMBRT facility at the Curie Institute

Objective.Proton radiotherapy's efficacy relies on an accurate relative stopping power (RSP) map of the patient to optimise the treatment plan and minimize uncertainties. Currently, a conversion of a Hounsfield Units map obtained by a common x-ray computed tomography (CT) is used to compute the RSP. This conversion is one of the main limiting factors for proton radiotherapy. To bypass this conversion a direct RSP map could be obtained by performing a proton CT (pCT). The focal point of this article is to present a proof of concept of the potential of fast pixel technologies for proton tracking at clinical facilities.Approach.A two-layer tracker based on the TJ-Monopix1, a depleted monolithic active pixel sensor (DMAPS) chip initially designed for the ATLAS, was tested at the proton minibeam radiotherapy beamline at the Curie Institute. The chips were subjected to 100 MeV protons passing through the single slit collimator (0.4×20mm2) with fluxes up to1.3×107p s-1 cm-2. The performance of the proton tracker was evaluated with GEANT4 simulations.Main results.The beam profile and dispersion in air were characterized by an opening of 0.031 mm cm-1, and aσx=0.172mm at the position of the slit. The results of the proton tracking show how the TJ-Monopix1 chip can effectively track protons in a clinical environment, achieving a tracking purity close to 70%, and a position resolution below 0.5 mm; confirming the chip's ability to handle high proton fluxes with a competitive performance.Significance.This work suggests that DMAPS technologies can be a cost-effective alternative for proton imaging. Additionally, the study identifies areas where further optimization of chip design is required to fully leverage these technologies for clinical ion imaging applications.

Assessment of the impact of the Russian-Ukrainian war on the agrarian potential in Kherson region

The relevance of the study lies in the need to assess the impact of the russian-Ukrainian war on the agrarian potential of the Kherson region due to significant economic and ecological losses, in particular, the destruction and contamination of soils, which requires urgent monitoring of their quality and restoration of fertility to restore the agrarian sphere of the region. The assessment of the impact of the russian-Ukrainian war on the agrarian potential of the Kherson region and the determination of the prospects for its post-war recovery were chosen as the purpose of this work. The research was carried out using general scientific and special methods of studying phenomena and processes: analysis and synthesis, system analysis, method of analogy and modelling, system analysis, methods of information collection and processing. Data from the NASA FIRMS databases, Sentinel-2 satellite images, as well as images of fires from the Giovanni resource were used as an information base for the analysis. Additionally, tracking results of MODIS, VIIRS and VIIRS 2 Suomi satellites are involved. A mutual correlation of the yield of agricultural crops and the Agricultural Stress Index was carried out, which proves the absence of a serious impact of drought on the yield of agricultural crops in the Kherson region, except for the driest years. The dependence between the yield of grain and leguminous crops and the application of fertilisers and pesticide treatment was established through the use of the mutual correlation function. A model of the influence of the application of mineral fertilisers and pesticides was built using the Cobb-Douglas econometric function, which allows predicting the optimal conditions for the introduction of agricultural production. Conclusions, proposals and practical recommendations will be useful in the development of relevant programs and measures aimed at the post-war restoration of agricultural soil and the general development of the country's agricultural production