Point Trajectory Research Articles

Rotation invariant GPS trajectory mining

Mining of GPS trajectories of moving vehicles and devices can provide valuable insights into urban systems, planning and operational applications. Understanding object motion often requires that the spatial-temporal matching of trajectories be invariant to shifting, scaling and rotation. To this end, Procrustes analysis enables to transform one data set of a trajectory to represent another set of data as closely as possible. We propose a novel shift-scale-rotation invariant Procrustes distance metric based on the Kabsch algorithm, which calculates the optimal rotation matrix by minimizing the root-mean squared deviation between two paired sets of points of trajectories or trajectory segments. We present two novel runtime efficient algorithms which are based on our proposed distance metric: 1) the sliding-shifting-scaling-Kabsch-rotation (S3KR) algorithm for detecting recurring short query patterns in longer motion trajectories and 2) a novel time series subsequence clustering algorithm to group GPS trajectory data and to discover prototypical patterns. We demonstrate the potential of our proposed sliding Procrustes analysis algorithms by applying it on real-world GPS trajectories collected in urban and rural areas from different transport modes, as well as on nautical GPS trajectories. We also demonstrate that our methods outperform the state of the art in accuracy and runtime on synthetic and real world data.

Minimally-invasive bedside catheter haematoma aspiration followed by local thrombolysis in spontaneous supratentorial intracerebral haemorrhage: a retrospective single-center study.

The role of surgery in the treatment of intracerebral haemorrhage (ICH) remains controversial. Whereas open surgery has failed to show any clinical benefit, recent studies have suggested that minimal invasive procedures can indeed be beneficial, especially when they are applied at an early time point. This retrospective study therefore evaluated the feasibility of a free-hand bedside catheter technique with subsequent local lysis for early haematoma evacuation in patients with spontaneous supratentorial ICH. Patients with spontaneous supratentorial haemorrhage of a volume of >30 mL who were treated with bedside catheter haematoma evacuation were identified from our institutional database. The entry point and evacuation trajectory of the catheter were based on a 3D-reconstructed CT scan. The catheter was inserted bedside into the core of the haematoma, and urokinase (5,000 IE) was administered every 6 h for a maximum of 4 days. Evolution of haematoma volume, perihaemorrhagic edema, midline-shift, adverse events and functional outcome were analyzed. A total of 110 patients with a median initial haematoma volume of 60.6 mL were analyzed. Haematoma volume decreased to 46.1 mL immediately after catheter placement and initial aspiration (with a median time to treatment of 9 h after ictus), and to 21.0 mL at the end of urokinase treatment. Perihaemorrhagic edema decreased significantly from 45.0 mL to 38.9 mL and midline-shift from 6.0 mm to 2.0 mm. The median NIHSS score improved from 18 on admission to 10 at discharge, and the median mRS at discharge was 4; the latter was even lower in patients who reached a target volume ≤ 15 mL at the end of local lysis. The in-hospital mortality rate was 8.2%, and catheter/local lysis-associated complications occurred in 5.5% of patients. Bedside catheter aspiration with subsequent urokinase irrigation is a safe and feasible procedure for treating spontaneous supratentorial ICH, and can immediately reduce the mass effects of haemorrhage. Additional controlled studies that assess the long-term outcome and generalizability of our findings are therefore warranted. [www.drks.de], identifier [DRKS00007908].

The Australian breaking scene and the Olympic Games: The possibilities and politics of sportification

In this article, we analyse the impact of breaking’s inclusion in the Olympics on the Australian breaking scene. We draw on our experiences as Australian breaking practitioners, as well as ethnographic field research conducted between 2018 and 2021, to show how Australian breakers have responded to, and made sense of, breaking becoming an Olympic sport. While some breakers see the Olympics as an opportunity and space for wider recognition, many have expressed concerns with the growing influence (and embrace) of transnational commercial organizations and institutional governing bodies in shaping and managing breaking’s future. Alongside concerns of an increasing sportification of breaking, this trajectory points towards an increasing loss of self-determination, agency and spontaneity for local Australian breakers and will have profound consequences for the way in which hip hop personhood is constantly ‘remade and renegotiated’ in Australia (Marie 2020: 4). Isolated from the major breaking hubs (North America/Asia/Europe), Australia’s breaking scene is marked by distinct, self-determined localized scenes separated from each other by the geographic expansiveness of this island-continent. Here, breaking is a space for those ‘othered’ by Australian institutions to express themselves and engage in new hierarchies of respect. We argue that breaking’s institutionalization via the Olympics will place breaking more firmly within this sporting nation’s hegemonic settler-colonial structures that rely upon racialized and gendered hierarchies. As such, in this article we discuss and examine how the Olympics impacts ongoing local, social and cultural productions and expressions of hip hop, and the distinct possibilities of breaking that enable its participants to ‘show and prove’ outside standardized, institutionalized rubrics.

Pitch Angle Error Measurement Technology for Track and Control Equipment Based on Trajectory

Due to the large support span of the pitching mechanism of the Fork-arm antenna pedestal of large phased array track and control equipment, the antenna array was deflected downward by its self-weight load. When measuring the pitch angle error, the pitch axis and the normal pointing angle of the array fluctuated, which significantly interfered with the evaluation results of common measuring methods for pitch angle error such as electronic goniometer. In view of the above problems, this paper proposed a pitch angle error measurement technology of track and control equipment based on motion trajectory. In this method, Target point for measurement was set in the appropriate area of the end face of the array skeleton, and the laser tracker was used to track and capture the spatial coordinates and trajectory of the target point in the process of pitching motion. The geometric model of the pitch angle algorithm was constructed based on the circular motion trajectory, which eliminated the influence of the fluctuation of the pitch axis and the normal pointing angle of the array on the pitch angle. The pitch angle error algorithm and data processing model were established, and the programmed rapid calculation and evaluation of the pitch angle deviation of track and control equipment were realized.

Key Points Trajectory and Predicted-Real Frames Distinction based Mirror and Glass Detection for Indoor 5G Signal Analysis

Mirror and glass are ubiquitous materials that heavily influence the transmission of indoor 5G signals with extremely high frequency. The existing vision system always tends to neglect them or misdiagnose them, which is unsuitable for 5G signal analysis in the 3D indoor environment. This paper proposes key points trajectory distinction and predicted-real frames distinction to detect mirror and glass regions in video sequences. Firstly, key points trajectory is used to extract the special motion information of reflection in mirror and glass region. Secondly, predicted and real frames distinction is used to remove the wrong detection region at the pixel level. Extensive experiments demonstrate that the proposed method achieves 40 - 50 % accuracy higher than another related state-of-the-art method for mirror and glass detection in the general 3D living environment.

An adaptive threshold fast DBSCAN algorithm with preserved trajectory feature points for vessel trajectory clustering

Vessel navigation pattern recognition plays an important role in the research of intelligent transportation on water. Clustering using the data stored in The Automatic Identification System (AIS) is a current research hotspot. However, there are three problems in the past clustering analysis. First, the traditional Douglas-Peucker (DP) Compression Algorithm exists feature point loss and trajectory distortion when compressing trajectories. Second, Dynamic Time Warping (DTW) and the density-based spatial clustering of applications with noise (DBSCAN) algorithm require high time cost. Finally, most of the studies ignore the interaction between parameters when choosing the parameters of DBSCAN. These problems seriously affect the efficiency and accuracy of clustering. To solve these problems, this paper improves the existing methods by (1) Adaptive selection of compression thresholds and trajectory feature points for each trajectory when using the DP algorithm ensures the realism of the compressed trajectory; (2) using the Fast-DTW algorithm to improve the computation speed and ensure the accuracy of trajectory similarity; (3) Self-selection of parameter combinations based on Silhouette Coefficient (SC) scores was achieved using the similarity distribution of the trajectories in combination with an improved K-Adaptive Nearest Neighbors (KANN). The experiments show that the proposed method can greatly reduce the time cost of clustering compared to the original method and significantly outperforms the three compared algorithms in terms of clustering effect images.

Vessel sailing route extraction and analysis from satellite-based AIS data using density clustering and probability algorithms

The vessel Automatic Identification System (AIS) data collected by satellites have the features of large coverage area and large data volume, and they are instantaneous discrete data rather than time-continuous data, so the data has large dispersion with many noise points. This poses a challenge for vessel sailing route extraction. This paper proposes a vessel sailing route extraction method which consists of the fast Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Kernel Density Estimation-based Outlier Factor (KDE-based OF) noise reduction algorithm. The method in this paper firstly improves the clustering discrimination method in the DBSCAN algorithm to separate trajectories in different directions. Secondly, this paper extracts a fast clustering algorithm based on the density clustering algorithm to reduce its computing time overhead with satellite big data. Finally, this paper proposes the KDE-based OF processing algorithm, which calculates the outlier probability distribution value of the trajectory points through the algorithm to eliminate the edge trajectory points with low probability distribution. Based on the actual satellite vessel AIS data, this paper conducts multi-method comparisons and performance analysis experiments. Experiments show that the proposed method has the best stability and advancement.

Onset of chaotic gravitational lensing in non-Kerr rotating black holes with quadrupole mass moment* *Supported by the National Natural Science Foundation of China (NNSFC, 12005077) and Guangdong Basic and Applied Basic Research Foundation (2021A1515012374). We also gratefully acknowledge the financial support from Brazilian agencies Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Fundação de Amparo à Pesquisa

In the electromagnetic channel, chaotic gravitational lensing is a peculiar phenomenon in strong gravitational lensing. In this study, we analyze the properties and emergence of chaotic gravitational lensing in the Manko-Novikov black hole spacetime. Aiming to better understand the underlying physics, we elaborate on the boundaries of the accessible region through analyses of the contours of the effective potentials. The latter is associated with the two roots of a quadratic equation. In particular, we explore its interplay with an ergoregion, which leads to specific features of the effective potentials, such as the emergence of a cuspy edge and the formation of a pocket, which serve as static constraints on the geodesics. Additionally, we investigate the properties of the radial and angular accelerations at the turning points in photon trajectories. The accelerations are further examined and may provide kinematic constraints on the geodesics, as argued herein. It is concluded that the onset of the chaotic lensing is significantly related to both constraints; as a result, an arbitrary slight deviation in the incident photon is significantly amplified during evolution through an extensive period, demonstrating the complexity in the highly nonlinear deterministic gravitational system.

PCDA™ SLAM-BASED TECHNOLOGY FOR POINT CLOUD AND TRAJECTORY OPTIMIZATION FOR AIRBORNE, LAND, AND INDOOR APPLICATIONS IN GNSS-DENIED ENVIRONMENTS

Abstract. This paper presents the results of assessing the performance of Trimble Applanix PCDA™ SLAM-based technology to simultaneously optimize any mobile mapping system trajectory and LiDAR point cloud data in a GNSS-denied environment. The simultaneous use of inertially-aided GNSS data along with LiDAR point clouds to optimally correct shifts and/or drifts in the trajectory in GNSS-denied environments is addressed in detail in this paper. A number of Trimble MX50 Mobile Mapping System data sets were acquired in Germany particularly to assess the performance of PCDA™. The land mobile mapping data sets were acquired in deep urban canyons which were purposely acquired that way to reach the most challenging land mobile mapping data sets in a GNSS-denied environment. The PCDA™ technology assessment results are presented in detail. In summary, the results show how LiDAR data can successfully be used to correct the trajectory shifts and drifts due to GNSS outages by simultaneously optimizing both point cloud and trajectory data.

Study of Rotation Speed Curve Optimization under the Three-Body Coupling Grinding Mode.

The three-body coupling grinding mode of a ball ensures the batch diameter variation and batch consistency of precision ball machining based on the principle of ball forming, resulting in a structure that is simply and feasibly controllable. The change in the rotation angle can be jointly determined using the fixed load of the upper grinding disc and the rotation speed coordination of the inner and outer discs of the lower grinding disc. Related to this, the rotation speed is an important index to guarantee grinding uniformity. To ensure the quality of three-body coupling grinding, this study aims to establish the best mathematical control model of the rotation speed curve of the inner and outer discs in the lower grinding disc. Specifically, it includes two aspects. First, the optimization of the rotation speed curve was mainly studied, and the machining process was simulated with three speed curve combinations: 1, 2, and 3. By analyzing the evaluation index of ball grinding uniformity, the results revealed that the third speed curve combination had the best grinding uniformity, and the three speed curve combinations were optimized on the basis of the traditional triangular wave speed curve. Furthermore, the obtained double trapezoidal speed curve combination not only achieved the traditionally verified stability performance but also overcame the shortcomings of the other speed curves. The mathematical model established in this way was equipped with a grinding control system, which improved the fine control ability of the rotation angle state of the ball blank under the three-body coupling grinding mode. It also obtained the best grinding uniformity and sphericity and laid a theoretical foundation for achieving a grinding effect that was close to the ideal circumstance during mass production. Second, via theoretical comparison and analysis, it was determined that the ball shape and sphericity deviation (SPD) were more accurate than the standard deviation (STD) of the two-dimensional trajectory point distribution. The SPD evaluation method was also investigated via the optimization analysis of the rotation speed curve by means of the ADAMAS simulation. The obtained results coincided with the STD evaluation trend, thus laying a preliminary foundation for subsequent applications.

Thermal failure analysis and control algorithm improvement for the active controlled mount with consideration of thermal–magnetic coupling

This paper proposes an improved narrowband filtered-x least mean square(FxLMS) algorithm to overcome the thermal failure problems of the active controlled mount(ACM). Firstly, the temperature-rising model and the thermal demagnetization model of the ACM are respectively developed. Combining these two models with the powertrain mounting system model, an analytical method is established for the thermal–magnetic coupling analysis of the ACM. Then a numerical simulation is carried out to obtain the permanent magnet(PM) temperature and the coil current. The ACM failure problem is discussed according to the working point trajectory. Finally, an improved algorithm is proposed. This algorithm is able to overcome the thermal failure problems by sacrificing part of the vibration isolation performance. The effectiveness of this algorithm is validated by numerical simulations and a comparison with conventional algorithms.

Field-road classification for GNSS recordings of agricultural machinery using pixel-level visual features

The understanding of agricultural machinery, whether it is used “in field” or “on road”, plays an essential role in optimizing the efficiency of cross-regional agricultural mechanization services that have been available in China for decades. With the widespread availability of Global Navigation Satellite System(GNSS)-enabled devices, developing a method for automatically identifying the activity associated with each point in GNSS recorded trajectories can significantly enhance the optimization of these services. In this paper, we propose a novel field-road classification method that utilizes two feature vectors to represent each point in a GNSS trajectory. The first feature vector is a statistical feature vector extracted with existing methods, while the second feature vector is a visual feature vector obtained through an image segmentation model. To extract visual features, we first convert each GNSS trajectory into a trajectory image that encodes the motion information (e.g., speed, direction) of each point and the spatiotemporal relationship between points. Then, an image segmentation model specifically for field-road classification was developed, which can effectively extract a visual feature vector for each pixel in a trajectory image, corresponding to each point in the GNSS trajectory. The image segmentation model used in our approach has a robust feature extraction capability that enhances the statistical feature representation of each point by adding pixel-level visual features. We evaluated the effectiveness of our multi-view field-road classification method using trajectories of corn, wheat, and paddy harvesters. The results demonstrated that our method achieved accuracy scores of 94.17%, 90.93%, and 83.43% for respective datasets. Our method outperformed state-of-the-art field-road classification methods, especially for trajectories with high-frequency GNSS acquisition.

Construction and Analysis of Space–Time Paths for Moving Polygon Objects Based on Time Geography: A Case Study of Crime Events in the City of London

Time geography considers that the motion of moving objects can be expressed using space–time paths. The existing time geography methods construct space-time paths using discrete trajectory points of a moving point object to characterize its motion patterns. However, these methods are not suitable for moving polygon objects distributed by point sets. In this study, we took a type of crime event as the moving object and extracted its representative point at each moment, using the median center to downscale the polygon objects distributed by the point sets into point objects with timestamps. On this basis, space–time paths were generated by connecting the representative points at adjacent moments to extend the application scope of space–time paths, representing the motion feature from point objects to polygon objects. For the case of the City of London, we constructed a space–time path containing 13 nodes for each crime type (n = 14). Then, each edge of the space–time paths was considered as a monthly vector, which was analyzed statistically from two dimensions of direction and norm, respectively. The results showed that crime events mainly shifted to the east and west, and crime displacement was the greatest in April. Therefore, space–time paths as proposed in this study can characterize spatiotemporal trends of polygon objects (e.g., crime events) distributed by point sets, and police can achieve improved success by implementing targeted crime prevention measures according to the spatiotemporal characteristics of different crime types.

The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea

The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by 1.8% and 1.2%, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management.

Annual Energy Production Design Optimization for PM Generators Considering Maximum Power Point Trajectory of Wind Turbines

Efficiency optimization is an important goal in the design of permanent magnet generators. However, traditional design optimization methods only focus on improving the rated efficiency without considering the annual cycle for overall efficiency improvement. To overcome this drawback, this paper presents a design optimization method for improving annual energy production (AEP) of wind direct-drive permanent magnet generators. Unlike the conventional efficiency optimization method that only improves the rated point efficiency, the proposed method improves the overall efficiency of the generator during the operating cycle by matching the maximum power point trajectory of the wind turbine. The periodic loss model of the permanent magnet generator is established and further constituted as the objective function to perform the optimization search using a genetic algorithm. Through simulation and experimental verification, the proposed method can obtain a higher AEP compared with the conventional design optimization method, and the proposed method can be extended to other variable speed power generation fields.

Local Corner and Motion Key Point Trajectory Extraction for Facial Forgery Identification

At present, the development of deep forgery technology has brought new challenges to media content forensics, and the use of deep forgery identification methods to identify forged audio and video has become a significant focus of research and difficulty. Deep forgery technology and forensic technology play a mutual game and promote each other’s development. This paper proposes a spatiotemporal local feature abstraction (STLFA) framework for facial forgery identification to solve the media industry challenges of deep forgery technology. To adequately utilize local facial features, we combine facial key points, key point movement, and facial corner points to detect forgery content. This paper establishes a spatiotemporal relation, which realizes face forgery detection by identifying abnormalities of facial keypoints and corner points for interframe judgments. Meanwhile, we utilize RNNs to predict the sequences from facial key point movement abnormalities and corner points for interframe. Experimental results show that our method achieves better performance than some existing methods and good anticompression forgery face detection performance on FF++.

Estimating traffic speed and speeding using passively collected big mobility data and a distributed computing framework

AbstractWith the increasing availability of location‐aware devices, passively collected big GPS trajectory data offer new opportunities for analyzing human mobility. Processing big GPS trajectory data, especially extracting information from billions of trajectory points and assigning information to corresponding road segments in road networks, is a challenging but necessary task for researchers to take full advantage of big data. In this research, we propose an Apache Spark and Sedona‐based computing framework that is capable of estimating traffic speeds for statewide road networks from GPS trajectory data. Taking advantage of spatial resilient distributed datasets supported by Sedona, the framework provides high computing efficiency while using affordable computing resources for map matching and waypoint gap filling. Using a mobility dataset of 126 million trajectory points collected in California, and a road network inclusive of all road types, we computed hourly speed estimates for approximately 600,000 segments across the state. Comparing speed estimates for freeway segments with speed limits, our speed estimates showed that speeding on freeways occurred mostly during the nighttime, while analysis of travel on residential roads showed that speeds were relatively stable over the 24‐h period.

Research on logistics Route Optimization based on AFSA

Based on the characteristics of logistics goods, in the process of logistics to choose a reasonable or optimal path, will save more logistics costs. In the process of using artificial fish swarm algorithm to find the optimal path planning, through the artificial fish swarm algorithm to improve the visual range, congestion factor and other factors, and record and mark the feasible solution, the target point of the fish swarm trajectory, form the feasible solution of the path planning and select the best solution, to achieve the optimal logistics path, reduce the cost of goods logistics.

Computer vision-based framework for pedestrian movement direction recognition

Pedestrians are the most critical and vulnerable moving objects on roads and public areas. Learning pedestrian movement in these areas can be helpful for their safety. To improve pedestrian safety and enable driver assistance in autonomous driver assistance systems, recognition of the pedestrian direction of motion plays an important role. Pedestrian movement direction recognition in real world monitoring and ADAS systems are challenging due to the unavailability of large annotated data. Even if labeled data is available, partial occlusion, body pose, illumination and the untrimmed nature of videos poses another problem. In this paper, we propose a framework that considers the origin and end point of the pedestrian trajectory named origin-end-point incremental clustering (OEIC). The proposed framework searches for strong spatial linkage by finding neighboring lines for every OE (origin-end) lines around the circular area of the end points. It adopts entropy and Qmeasure for parameter selection of radius and minimum lines for clustering. To obtain origin and end point coordinates, we perform pedestrian detection using the deep learning technique YOLOv5, followed by tracking the detected pedestrian across the frame using our proposed pedestrian tracking algorithm. We test our framework on the publicly available pedestrian movement direction recognition dataset and compare it with DBSCAN and Trajectory clustering model for its efficacy. The results show that the OEIC framework provides efficient clusters with optimal radius and minlines.

Research on the inverse kinematic algorithm of the robot arm of the minecart

A reverse kinematics solution algorithm for the robot arm of the minecart is proposed based on the structure and motion characteristics of the robot arm, the geometric method is combined with the algebra method, and the trajectory of each joint point on the mechanical arm is calculated inversely by using the motion trajectory of the roller on the mining wheel to reverse-calculate the trajectory of each joint point on the mechanical arm, and the joint angle of the mechanical arm is obtained according to the trajectory of each joint point. The results are compared and analyzed through the Robots Toolbox toolbox in Matlab to verify the correctness and validity of the inverse calculation results. It lays a foundation for future research on key issues such as path planning and trajectory tracking control of the robot arm of the minecart.