Search Results (26)

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article

Against the backdrop of the deep integration of national fitness and sports science, this study addresses the lack of standardized movement assessment in yoga training by proposing an intelligent analysis system that integrates an improved YOLOv11-ECA detector with the DeepSORT tracking algorithm. A dynamic adaptive anchor mechanism and an Efficient Channel Attention (ECA) module are introduced, while the depthwise separable convolution in the C3k2 module is optimized with a kernel size of 2. Furthermore, a Parallel Spatial Attention (PSA) mechanism is incorporated to enhance multi-target feature discrimination. These enhancements enable the model to achieve a high detection accuracy of 98.6% mAP@0.5 while maintaining low computational complexity (2.35 M parameters, 3.11 GFLOPs). Evaluated on the SND Sun Salutation Yoga Dataset released in 2024, the improved model achieves a real-time processing speed of 85.79 frames per second (FPS) on an RTX 3060 platform, with an 18% reduction in computational cost compared to the baseline. Notably, it achieves a 0.9% improvement in AP@0.5 for small targets (<20 px). By integrating the Mars-smallCNN feature extraction network with a Kalman filtering-based trajectory prediction module, the system attains 58.3% Multiple Object Tracking Accuracy (MOTA) and 62.1% Identity F1 Score (IDF1) in dense multi-object scenarios, representing an improvement of approximately 9.8 percentage points over the conventional YOLO+DeepSORT method. Ablation studies confirm that the ECA module, implemented via lightweight 1D convolution, enhances channel attention modeling efficiency by 23% compared to the original SE module and reduces the false detection rate by 1.2 times under complex backgrounds. This study presents a complete “detection–tracking–assessment” pipeline for intelligent sports training. Future work aims to integrate 3D pose estimation to develop a closed-loop biomechanical analysis system, thereby advancing sports science toward intelligent decision-making paradigms. Full article

In the future, radar will encounter a more intricate and ever-changing electromagnetic interference environment. Consequently, one crucial trajectory for radar system evolution is the incorporation of network and cognition capabilities to meet these emerging challenges. The traditional frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is rendered ineffective due to occurrences of frequency spectrum interference and main-lobe deceptive interference with arbitrary time delays. Therefore, a cognitive FDA-MIMO radar network (CFDA-MIMORN) transmit element selection algorithm is introduced. At first, the target is discriminated from the false targets. The Kalman filter is used to track the target, then available information is used to infer the target’s position in the next time step. The finite transmit elements of the radar network are organized to enhance tracking performance, especially in the presence of frequency spectrum interferences. The numerical simulations demonstrate that the proposed CFDA-MIMORN can effectively discriminate the true target from false targets, and optimize the allocation of transmit elements to avoid interferences, resulting in improved tracking accuracy. Full article

Due to its ease of implementation without an additional transmitter, radar communication integrated systems using conventional communication signals have become an effective means for monitoring unmanned aerial vehicles and other aircraft. However, their non-ideal radar signal form causes strong signals to mask weak signals, and clutter suppression is required to detect a target. As the energy of a target signal is extremely low and conventional clutter suppression methods have limited performance, the residual clutter persists after the time-varying clutter is suppressed, resulting in many false alarm points on the processed range–Doppler (RD) map. The two-dimensional distribution of these false alarms on the range–Doppler is very similar to that of the target and difficult to discriminate, which seriously affects target detection and tracking. To reduce false alarm points and improve the performance of target detection, the difference in the spatial–temporal stationarity between a target signal and clutter in a short time is discussed in this paper; a radar false alarm suppression method based on a target’s spatial–temporal stationarity is proposed by using the difference in the stationarity between the target signal and false alarm points in the range, Doppler, energy and azimuth. In this algorithm, to ensure the short-time stationarity of the target, the RD map of the short-time interval sub-frames is obtained by using the sliding matching filtering method and the peak points are extracted. Then, the Mahalanobis distance between the peak points of each sub-frame is used to eliminate the false alarm points. Finally, the false alarm points are further eliminated by target tracking and the real target information to improve the radar target detection performance. The simulation experiments show that this method can eliminate more than 90% of the false alarms points while maintaining the target detection performance. The analysis of actual data obtained from the field experiment indicate that the implementation of this algorithm, which effectively suppresses false alarms, leads to improved target detection outcomes. These enhancements can facilitate the tracking of UAVs and other aircraft. Full article

In the digital age, spreading false information has a far-reaching impact on various areas, such as society, politics, and the economy. With the popularization of applications of text generation models, the cost of producing false information has significantly decreased, making it challenging for human beings to screen it. Therefore, research on detection screening and early warning control for model-generated false information becomes particularly important. In this paper, we propose a model-generated false information detection and control system based on blockchain. Firstly, we design a model-generated false information detection method combining model-generated text discrimination based on a self-attention network and text similarity detection based on a twin network. Secondly, we construct a blockchain-based model-generated false information control and traceability system. It utilizes the proposed detection algorithm to provide early warning and control of model-generated false information involving important and sensitive events before social network release. For information judged to be model-generated false, the stored data on the blockchain is utilized to track and trace the publisher. Ultimately, experimental tests prove that the proposed detection method improves the accuracy of false information detection. In addition, the operational efficiency of the prototype system can meet quality of service requirements. Full article

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment. Full article

The Integrated Track Splitting (ITS) is a multi-scan algorithm for target tracking in a cluttered environment. The ITS filter models each track as a set of mutually exclusive components, usually in the form of a Gaussian Mixture. The purpose of this research is to determine the limits of the ‘endurance’ of target tracking of the known ITS algorithm by analyzing the impact of target detection probability. The state estimate and the a-posteriori probability of component existence are computed recursively from the target existence probability, which may be used as a track quality measure for false track discrimination (FTD). The target existence probability is also calculated and used for track maintenance and track output. This article investigates the limits of the effectiveness of ITS multi-target tracking using the method of theoretical determination of the dependence of the measurements likelihood ratio on reliable detection and then practical experimental testing. Numerical simulations of the practical application of the proposed model were performed in various probabilities of target detection and dense clutter environments. Additionally, the effectiveness of the proposed algorithm in combination with filters for various types of maneuvers using Interacting Multiple Model ITS (IMMITS) algorithms was comparatively analyzed. The extensive numerical simulation (which assumes both straight and maneuvering targets) has shown which target tracking limits can be performed within different target detection probabilities and clutter densities. The simulations confirmed the derived theoretical limits of the tracking efficiency of the ITS algorithm up to a detection probability of 0.6, and compared to the IMMITS algorithm up to 0.4 in the case of target maneuvers and dense clutter environments. Full article

Multiple-object tracking (MOT) is a fundamental task in computer vision and is widely applied across various domains. However, its algorithms remain somewhat immature in practical applications. To address the challenges presented by complex scenarios featuring instances of missed detections, false alarms, and frequent target switching leading to tracking failures, we propose an approach to multi-object tracking utilizing KC-YOLO detection and an identity validity discrimination module. We have constructed the KC-YOLO detection model as the detector for the tracking task, optimized the selection of detection frames, and implemented adaptive feature refinement to effectively address issues such as incomplete pedestrian features caused by occlusion. Furthermore, we have introduced an identity validity discrimination module in the data association component of the tracker. This module leverages the occlusion ratio coefficient, denoted by “k”, to assess the validity of pedestrian identities in low-scoring detection frames following cascade matching. This approach not only enhances pedestrian tracking accuracy but also ensures the integrity of pedestrian identities. In experiments on the MOT16, MOT17, and MOT20 datasets, MOTA reached 75.9%, 78.5%, and 70.1%, and IDF1 reached 74.8%, 77.8%, and 72.4%. The experimental results demonstrate the superiority of the methodology. This research outcome has potential applications in security monitoring, including public safety and fire prevention, for tracking critical targets. Full article

Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable. Full article

Approaching space target tracking is a typical and challenging mission in the space situational awareness (SSA) field. As the space-based radar is able to monitor the space targets of interest full-weather all-time, the space-based radar system is utilized in this paper. However, most multi-target tracking (MTT) filters in target tracking studies merely utilize the location or narrow measurements, and many potentially valuable electromagnetic scattering characteristics are missed, which leads to space target false tracking problems. The space-based radar transmits a wide-band signal, and the measured high-resolution range profile (HRRP) information is an effective characteristic for different target discrimination. Therefore, the HRRP characteristics of space targets are implemented into the update recursion of the MTT filter, which can be utilized to improve the tracking performance. Then, to predict the target HRRP sequence, the geometrical theory of diffraction (GTD) model is utilized. Additionally, a modified spatial spectrum method with a novel covariance matrix is designed to improve the scattering parameter estimation accuracy. Finally, an adapting threshold is devised for merging the Gaussian mixture (GM) components weights. The proposed threshold is on the basis of the proposed HRRP characteristic-aided probability hypothesis density (PHD) filter, and it can tackle the problem of space target discrimination. Simulation results validate the effectiveness and robustness of the proposed probability hypothesis density (HGI-PHD) filter aided by HRRP information and improved with GM weights. Full article

Aiming at the problems of big noise, lots of false targets, and accurate time extraction while tracking a flying target in the signal from a sky screen sensor, a flying target recognition and time extraction method is proposed, based on wavelet transformation. The noisy signal output by the sky screen sensor is filtered with wavelet transformation to filter out some high-frequency components; the filter is designed to handle the signal time frequency characteristics of the flying target and noise. To improve the recognition efficiency of whether the signal includes tracking of the flight target, based on a two-class discriminant model, the wavelet Fisher discriminant method is used to construct the feature vector of the false target and the flying target signal, and the recognition method of the flying target signal is studied. According to the wavelet modulus maxima theory, the single target signal is isolated, and the time moment of the flying target passing through the detection screen is calculated. The velocities calculated based on the flying target signal recognition method proposed in this paper and based on the least-mean-squares algorithm of the traditional sky screen sensor velocity measurement system are compared with the net target velocity measurement system. The results show that the velocity data obtained by the method in this paper are closer to the true value of the target flight velocity, and the average error between the velocity value obtained by the method in this paper and the standard net target velocity measurement system is less than 0.954 m/s, which verifies the superiority of the method proposed in this paper. Full article

The new mode of Circular Synthetic Aperture Radar (CSAR) has several advantages including multi-aspect and long-time observation, which can generate high-frame-rate image sequences to detect moving targets with a single-channel system. Nonetheless, due to CSAR being sensitive to 3D structures, static high targets are observed in scene display rotational motion within CSAR subaperture image sequences. Such motion can cause false alarms rising when utilizing image sequence-based moving target detection methods like logarithm background subtraction (LBS). To address this issue, this paper first thoroughly analyzes the moving target and static high target’s difference for the trajectory in an image sequence. Two new trajectory features of the rotation angle and moving distance are proposed to differentiate them. Based on the features, a new false alarm suppression method is proposed. The method first utilizes LBS to obtain coarse binary detection results comprising both moving and static high targets, then employs morphological filtering to eliminate noise. Next, DBSCAN and target tracking steps are employed to extract the trajectory features of the target and false alarm. Finally, false alarms are suppressed with trajectory-based feature discriminators to output detection results. The W-band CSAR open dataset is used to validate the proposed method’s effectiveness. Full article

In an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid track loss, we use the Markov-chain-two (MC2) model that allows the propagation of target existence through the occlusion region. We utilized the MC2 model in linear multi-target tracking based on the integrated probabilistic data association (LMIPDA) and proposed a modified integrated algorithm referred to here as LMIPDA-MC2. We consider a three-dimensional surveillance for tracking occluded targets, such as unmanned aerial vehicles (UAVs) and other autonomous vehicles at low altitude in clutters. We compared the results of the proposed method with existing Markov-chain model based algorithms using Monte Carlo simulations and practical experiments. We also provide track retention and false-track discrimination (FTD) statistics to explain the significance of the LMIPDA-MC2 algorithm. Full article

Satellite-based video enables potential vehicle monitoring and tracking for urban traffic management. However, due to the tiny size of moving vehicles and cluttered background, it is difficult to distinguish actual targets from random noise and pseudo-moving objects, resulting in low detection accuracy. In contrast to the currently overused deep-learning-based methods, this study takes full advantage of the geometric properties of vehicle tracklets (segments of moving object trajectory) and proposes a tracklet-feature-based method that can achieve high precision and high recall. The approach is a two-step strategy: (1) smoothing filtering is used to suppress noise, and then a non-parametric-based background subtracting model is applied for obtaining preliminary recognition results with high recall but low precision; and (2) generated tracklets are used to discriminate between true and false vehicles by tracklet feature classification. Experiments and evaluations were performed on SkySat and ChangGuang acquired videos, showing that our method can improve precision and retain high recall, outperforming some classical and deep-learning methods from previously published literature. Full article

Pumice rafts are considered to be a long-range drifting agent that promotes material exchange and the dispersal of marine species. Large ones can also interfere with vessel navigation and have a negative impact on the social economy and marine ecosystems. Synoptic observations from the Multispectral Instrument (MSI) on-board Sentinel-2, with a spatial resolution of up to 10 m, provide an excellent means to monitor and track pumice rafts. In this study, the use of a Spectral-Feature-Based Extraction (SFBE) algorithm to automatically discriminate and extract pumice on the ocean surface from submarine volcano eruptions was proposed. Specifically, a Pumice Raft Index (PRI) was developed based on the spectral signatures of pumice in MSI imagery to identify potential pumice features. After pre-processing, the PRI image was then subjected to a series of per-pixel and object-based processes to rule out false-positive detections, including shallow water, striped edges, mudflats, and cloud edges. The SFBE algorithm showed excellent performance in extracting pumice rafts and was successfully applied to extract pumice rafts near the Fiji Yasawa islands in 2019 and Hunga Tonga island in 2022, with an overall pumice extraction accuracy of 95.5% and a proportion of pixels mis-extracted as pumice of <3%. The robustness of the algorithm has also been tested and proved through applying it to data and comparing its output to results from previous studies. The timely and accurate detection of pumice using the algorithm proposed here is expected to provide important information to aid in response actions and ecological assessments, and will lead to a better understanding of the fate of pumice. Full article