Trajectories Of Moving Objects Research Articles

Design and Application of Basketball Tactical Drill System Based on Motion Trajectory Analysis Algorithm

A basketball tactical drill system based on motion trajectory analysis algorithms employs advanced computational methods to enhance training effectiveness and strategy development. By utilizing these algorithms to analyze player movements and interactions during drills, coaches can gain valuable insights into team dynamics, spatial awareness, and decision-making processes. This analysis enables coaches to tailor drills to specific tactical objectives, such as improving defensive rotations, offensive spacing, or fast break execution. Additionally, the system can provide real-time feedback to players, helping them understand their positioning and movement patterns to refine their skills and decision-making abilities. This paper presents the design and implementation of a Basketball Tactical Drill System leveraging advanced object detection and motion trajectory analysis algorithms. The system aims to enhance basketball training by providing coaches and players with real-time insights into tactical drills and player performance. Two key algorithms, Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT), are utilized to accurately identify and track basketballs, players, and other relevant objects during training sessions. Experimental results demonstrate the effectiveness of the proposed system, showcasing high detection accuracy, minimal false positives, and competitive processing times across various scenarios and tactical drills. The system leverages Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT) algorithms to achieve exceptional detection accuracy, with an average of 93% across various scenarios and tactical drills. False positives are minimized, with an average of 12 per scenario. Processing times remain competitive, averaging 27 milliseconds per scenario. Integration of motion trajectory analysis provides coaches with invaluable real-time insights into player movements and interactions, facilitating data-driven coaching strategies. The system's versatility is demonstrated through successful application in a range of drills, resulting in performance improvements averaging 20%.

Identification of the object motion trajectory in a digital image using the Kalman filter

The paper concerns identification of the object motion trajectories in a digital image. An image analysis procedure was used consisting of two methods: BLOB identification and Kalman filter. BLOB identification is the superior method, and the Kalman filter is an alternative one, used to determine the motion trajectories only in the event of incorrect BLOB identification. Data from the correct BLOB identification are used on an ongoing basis in the process of training the Kalman filter. In this way, the filter becomes a model of a physical object that allows predicting the future state based on the object's current behaviour. The use of two complementary image analysis methods allows to determine the motion trajectories of the object's characteristic points in conditions of uneven scene lighting. The effectiveness of the method was confirmed in experimental rests.

A disposable cup inspired smart floor for trajectory recognition and human-interactive sensing

Smart floor is an indispensable component of future smart buildings, it is urgent to develop a low-cost, self-powered, and high reliability smart floor. Herein, we propose a disposable cups inspired self-powered smart floor (DCIS-floor) for trajectory recognition and human-interactive sensing. The conical surface of the cup-shaped triboelectric nanogenerator (TENG) is greater than the projected area, resulting in an increased working area of functional materials on a limited floor. This enables more power generation units to be arranged on the limited floor while ensuring that each unit can generate sufficient electricity. Both pressure and shear force are applied as two conical surfaces contact, increasing the degree of contact between functional materials while avoiding excessive frictional force and wear during working process. Compared to cylindrical structures, conical structures offer greater flexibility in contact-separation without intricate machining and assembly, which is ideal for efficient large-area manufacturing. In the experiments, DCIS-floor achieves object motion trajectory recognition, visual recognition based trajectory wireless sensing, and pressure distribution sensing functions. Utilizing a convolutional neural network for data analysis, DCIS-floor realizes personnel identification. This work provides an effective method for smart floors in the safety monitoring, intelligent identification, and emergency rescue of future smart buildings.

Super-resolution Localization Problem Based on the Mobile Scenario

Target positioning has always been a hot research topic in the field of sensors. In recent years, radar detection technology has become increasingly mature, resulting in a large number of radar sensors have begun to be used in traffic, security, medical and other fields for target detection. This paper mainly studies the super-resolution localization problem of FMCW FMCW in moving scenes. It analyzes the echo data by mathematical modeling to obtain the distance and angle information of the object to find the place of the object. To restore the motion trajectory of objects more realistically and achieve super-resolution positioning, a super-resolution radar positioning model based on Kriging interpolation based on the Fourier spectrum is established. By constructing a frame of intermediate frequency data Fourier spectrum tensor space, Fourier spectrum analysis is used to obtain the spectral characteristics of spatiotemporal changes when objects move. The Fourier spectrum surface is reasonably predicted through the statistical method - kriging interpolation. More Fourier spectrum information of objects in the time dimension can be obtained, and then more object points can be restored to achieve super-resolution positioning. This is widely used in the automotive field, which develops autonomous driving systems to help drivers better control the vehicle and reduce the probability of car accidents.

Dynamic Object Tracking Based on Triplet Relationship Guided Sampling Consensus Algorithm

Compared with the traditional methods based on the prediction of moving object trajectory, the method based on image registration has the advantage of not relying on the object motion equation to track the object. However, in the case of a large number of outliers, the image matching method has the defects of inadequate filtering of outliers or erroneous filtering of incomers due to the limitations of spatial constraints of feature matching pairs, which will make the tracking of flying objects inaccurate or impossible. During the flight, the object may be in a similar background and the image shooting angle is different, which makes the object imaging angle change, resulting in a large number of outlier interference. Because of the above situation, this paper adopted an improved TRESAC algorithm based on RANSAC, which used a feature matching method based on a triplet relationship to effectively filter outliers and then adopted an initial data subset selection strategy to increase its robustness. Experimental results show that the TRESAC algorithm can filter outliers quickly and accurately, and the object is still tracked effectively under the condition of similar background and pose change.

Weak non-line-of-sight target echoes extraction without accumulation.

Non-line-of-sight (NLOS) technology has been rapidly developed in recent years, allowing us to visualize or localize hidden objects by analyzing the returned photons, which is expected to be applied to autonomous driving, field rescue, etc. Due to the laser attenuation and multiple reflections, it is inevitable for future applications to separate the returned extremely weak signal from noise. However, current methods find signals by direct accumulation, causing noise to be accumulated simultaneously and inability of extracting weak targets. Herein, we explore two denoising methods without accumulation to detect the weak target echoes, relying on the temporal correlation feature. In one aspect, we propose a dual-detector method based on software operations to improve the detection ability for weak signals. In the other aspect, we introduce the pipeline method for NLOS target tracking in sequential histograms. Ultimately, we experimentally demonstrated these two methods and extracted the motion trajectory of the hidden object. The results may be useful for practical applications in the future.

Haptic Exploration of Unknown Objects for Robust In-Hand Manipulation.

Human-like robot hands provide the flexibility to manipulate a variety of objects that are found in unstructured environments. Knowledge of object properties and motion trajectory is required, but often not available in real-world manipulation tasks. Although it is possible to grasp and manipulate unknown objects, an uninformed grasp leads to inferior stability, accuracy, and repeatability of the manipulation. Therefore, a central challenge of in-hand manipulation in unstructured environments is to acquire this information safely and efficiently. We propose an in-hand manipulation framework that does not assume any prior information about the object and the motion, but instead extracts the object properties through a novel haptic exploration procedure and learns the motion from demonstration using dynamical movement primitives. We evaluate our approach by unknown object manipulation experiments using a human-like robot hand. The results show that haptic exploration improves the manipulation robustness and accuracy significantly, compared to the virtual spring framework baseline method that is widely used for grasping unknown objects.

Meta-Learning Based Classification for Moving Object Trajectories in Mobile IoT

The proliferation and ubiquity of GPS-enabled mobile Internet of Things (IoT) devices (e.g., drones and unmanned robotic devices) across many disciplines has generated substantial interest in the analysis and mining of trajectory data for enhancing public services such as air pollution monitoring and road safety. Among all trajectory analysis techniques in mobile IoT, classifying moving device trajectories is a fundamental research problem due to its importance for numerous important mobile IoT applications such as travel demand analysis and animal migration patterns. However, existing classification methods for moving object trajectories either are dynamic programming problems (such as similarity-based methods with dynamic time wrapping or Fréchet distance) with a quadratic time complexity in all cases, or need abundant labeled samples to training classification model, thus limit the scalability of these methods for given resource-constrained mobile IoT devices. In this study, we propose a deep learning-based approach for trajectory classification in mobile IoT with linear time complexity by firstly encoding a trajectory as a vector via deep representation learning, then utilizing a meta-learning based approach to learn the ability of classifying trajectory with few annotated samples. Experiments on a massive trajectory dataset show that the proposed approach outperforms state-of-the-art baselines consistently and significantly.

Discovering Geographical Flock Patterns of CO2 Emissions in China Using Trajectory Mining Techniques

Carbon dioxide (CO2) emissions are considered a significant factor that results in climate change. To better support the formulation of effective policies to reduce CO2 emissions, specific types of important emission patterns need to be considered. Motivated by the flock pattern that exists in the domain of moving object trajectories, this paper extends this concept to a geographical flock pattern and aims to discover such patterns that might exist in CO2 emission data. To achieve this, a spatiotemporal graph (STG)-based approach is proposed. Three main parts are involved in the proposed approach: generating attribute trajectories from CO2 emission data, generating STGs from attribute trajectories, and discovering specific types of geographical flock patterns. Generally, eight different types of geographical flock patterns are derived based on two criteria, i.e., the high-low attribute values criterion and the extreme number-duration values criterion. A case study is conducted based on the CO2 emission data in China on two levels: the province level and the geographical region level. The results demonstrate the effectiveness of the proposed approach in discovering geographical flock patterns of CO2 emissions and provide potential suggestions and insights to assist policy making and the coordinated control of carbon emissions.

Energy-Efficient Object Tracking Using Adaptive ROI Subsampling and Deep Reinforcement Learning

Recent innovations in ROI camera systems have opened up the avenue for exploring energy optimization techniques like adaptive subsampling. Generally speaking, image frame capture and read-out demand high power consumption. ROI camera systems make it possible to exploit the inverse relation between energy consumption and spatiotemporal pixel readout to optimize the power efficiency of the image sensor. To this end, we develop a reinforcement learning (RL) based adaptive subsampling framework which predicts ROI trajectories and reconfigures the image sensor on-the-fly for improved power efficiency of the image sensing pipeline. In our proposed framework, a pre-trained convolutional neural network (CNN) extracts rich visual features from incoming frames and a long short-term memory (LSTM) network predicts the region of interest (ROI) and subsampling pattern for the consecutive image frame. Based on the application and the difficulty level of object motion trajectory, the user can utilize either the predicted ROI or coarse subsampling pattern to switch off the pixels for sequential frame capture, thus saving energy. We have validated our proposed method by adapting existing trackers for the adaptive subsampling framework and evaluating them as competing baselines. As a proof-of-concept, our method outperforms the baselines and achieves an average AUC score of 0.5090 on three benchmarking datasets. We also characterize the energy-accuracy tradeoff of our method vs. the baselines and show that our approach is best suited for applications that demand both high visual tracking precision and low power consumption. On the TB100 dataset, our method achieves the highest AUC score of 0.5113 out of all the competing algorithms and requires a medium-level power consumption of approximately 4 W as per a generic energy model and an energy consumption of 1.9 mJ as per a mobile system energy model. Although other baselines are shown to have better performance in terms of power consumption, they are ill-suited for applications that require considerable tracking precision, making our method the ideal candidate in terms of power-accuracy tradeoff.

Digital image stabilization for the experimental verification of object motion trajectories

The article presents digital stabilization procedure of the image recorded with a camera performing unintentional spatial movement in relation to the scene. This method consists in carrying out translational and rotational transformations and scaling all frames in relation to the first one, using the tools of the Matlab environment. The image stabilization procedure allows to effectively improve the image to make it suitable for the analysis of the tracked objects trajectories on the scene, observed from the camera located on the rotor drone.

Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification

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.

Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina.

Motion sensing is a critical aspect of vision. We studied the representation of motion in mouse retinal bipolar cells and found that some bipolar cells are radially direction selective, preferring the origin of small object motion trajectories. Using a glutamate sensor, we directly observed bipolar cells synaptic output and found that there are radial direction selective and non-selective bipolar cell types, the majority being selective, and that radial direction selectivity relies on properties of the center-surround receptive field. We used these bipolar cell receptive fields along with connectomics to design biophysical models of downstream cells. The models and additional experiments demonstrated that bipolar cells pass radial direction selective excitation to starburst amacrine cells, which contributes to their directional tuning. As bipolar cells provide excitation to most amacrine and ganglion cells, their radial direction selectivity may contribute to motion processing throughout the visual system.

Vehicular Trajectory Classification and Traffic Anomaly Detection in Videos Using a Hybrid CNN-VAE Architecture

Visual surveillance has become indispensable in the evolution of Intelligent Transportation Systems (ITS). Video object trajectories are key to many of the visual surveillance applications. Classifying varying length time series data such as video object trajectories using conventional neural networks, can be challenging. In this paper, we propose trajectory classification and anomaly detection using a hybrid Convolutional Neural Network (CNN) and Variational Autoencoder (VAE) architecture. First, we introduce a high level features for varying length object trajectories using color gradient representation. In the next stage, a semi-supervised way to annotate moving object trajectories extracted using Temporally Incremental Gravitational Model (TIGM) is used for class labeling. For training, anomalous trajectories are identified using t-Distributed Stochastic Neighbor Embedding (t-SNE). Finally, a hybrid CNN-VAE architecture has been proposed for trajectory classification and anomaly detection. The results obtained using publicly available surveillance video datasets reveal that the proposed method can successfully identify traffic anomalies such as violations in lane driving, sudden speed variations, abrupt termination of vehicle during movement, and vehicles moving in wrong directions. The accuracy of trajectory classification improves by a margin of 1-6% against popular neural networks-based classifiers across various datasets using the proposed high-level features. The gradient representation also improves the anomaly detection accuracy significantly (30-35%). Code and dataset can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/santhoshkelathodi/CNN-VAE</uri> .

Simulation of Multimedia Visual Image Motion Track Marking Based on Artificial Bee Colony Algorithm

With the rapid development of electronic technology, network technology, and multimedia processing technology, people pay more and more attention to the surrounding environment. Paying attention to and handling emergencies have also become one of the main points of attention. Through the combination of multimedia and monitoring system, the collected image data are processed and integratedly controlled by the computer platform, and an intelligent monitoring system for traffic road safety can be obtained. Due to the advantages of multimedia visual image motion trajectory identification technology in road traffic safety, it can effectively prevent the chaos and influence caused by humans in chaotic situations, making it widely used. By identifying the motion trajectories of different objects, road emergencies can be effectively prevented and countermeasures can be established. Based on this, this study proposes the artificial bee colony (ABC) algorithm to identify the motion trajectory in the visual image, but the standard ABC algorithm is too slow to converge in the later stage of trajectory recognition. Therefore, this study is based on the standard ABC algorithm. The trajectory recognition simulation shows that the optimized algorithm accelerates the convergence speed and the ability of the global optimal solution in the trajectory recognition process. Then, the optimized ABC algorithm is used to identify the multimedia visual image trajectory. The experiment shows that the average correct rate of feature extraction is also about 98%. The data identification results also show that the optimized ABC algorithm fits the real trajectory better in the trajectory identification. The running time of the algorithm is shorter than the running time of other comparison algorithms, which fully shows the accuracy and superiority of the algorithm in this study.

Optimization of 3D Virtual Reality Technology in High School Physics Direct-Type Teaching

Virtual reality technology is a computer simulation that allows the creation and experience of virtual worlds, using computers to generate a simulated environment into which the user is immersed. Through a theoretical exposition of 3D virtual reality technology, the elements of teaching and learning and virtual reality technology are analyzed in terms of abstract virtual cognition and concrete modeling techniques, and the forms of application of virtual reality technology in high school physics teaching are described, along with associated educational technology theories. The era of core literacy development poses new challenges to basic teaching and requires new teaching methods and approaches to adapt. The development of 3D virtual reality technology has provided new opportunities for teaching reform. Firstly, the unique charm of virtual reality technology itself has deeply attracted many experts and scholars to explore its application in pedagogy. Secondly, the scale and effect of virtual reality technology in pedagogy applications are still far from the ideal goal in people’s mind, and further exploration and research are needed in the theoretical support of educational technology research. Thirdly, in the process of high school basic teaching, on the one hand, basic experiments play a crucial role, and on the other hand, transforming abstract basic principles and concepts into concrete visible and operable teaching methods is also an important direction for its future development. Combined with the development tools and software to build processes and models, the knowledge of electricity and mechanics in high school physics is modeled in 3Ds MAX and Unity3D to provide technical support for the application of virtual reality in high school physics teaching and to promote the reform of high school physics teaching. Based on an in-depth analysis of virtual reality technology in the construction of teaching resources, this paper explores the feasibility of using software systems and hardware facilities of 3D virtual reality technology in high school physics teaching, such as experimental demonstration, geometric modeling, and 3D graphic transformation. It fits the cognitive level of high school students, constructs 3D virtual scenes and virtual object motion trajectory models that meet the requirements of the high school physics curriculum, and promotes the application of virtual reality technology in high school physics teaching and visualizing abstract physics knowledge, with clear student understanding and significant improvement in learning efficiency.

Optimizing Moving Object Trajectories from Roadside Lidar Data by Joint Detection and Tracking

High-resolution traffic data, comprising trajectories of individual road users, are of great importance to the development of Intelligent Transportation Systems (ITS), in which they can be used for traffic microsimulations and applications such as connected vehicles. Roadside laser scanning systems are increasingly being used for tracking on-road objects, for which tracking-by-detection is the widely acknowledged method; however, this method is sensitive to misdetections, resulting in shortened and discontinuous object trajectories. To address this, a Joint Detection And Tracking (JDAT) scheme, which runs detection and tracking in parallel, is proposed to mitigate miss-detections at the vehicle detection stage. Road users are first separated by moving point semantic segmentation and then instance clustering. Afterwards, two procedures, object detection and object tracking, are conducted in parallel. In object detection, PointVoxel-RCNN (PV-RCNN) is employed to detect vehicles and pedestrians from the extracted moving points. In object tracking, a tracker utilizing the Unscented Kalman Filter (UKF) and Joint Probabilistic Data Association Filter (JPDAF) is used to obtain the trajectories of all moving objects. The identities of the trajectories are determined from the results of object detection by using only a certain number of representatives for each trajectory. The developed scheme has been validated at three urban study sites using two different lidar sensors. Compared with a tracking-by-detection method, the average range of object trajectories has been increased by &gt;20%. The approach can also successfully maintain continuity of the trajectories by bridging gaps caused by miss-detections.

Data Mining Algorithm for Moving Object Trajectory

Aiming at the poor effect and long recognition time of data mining algorithm for moving target trajectory recognition, a data mining algorithm based on improved Hausdorff distance is proposed. The position and angle of abnormal trajectory data are detected by calculating the distance between trajectory classification and sub trajectory line segments, and the trajectory unit is established by using the improved Hausdorff distance algorithm to optimize the similarity matching structure. Experimental results show that the algorithm has low error pruning rate in identifying moving target trajectory, improves the detection efficiency of moving target trajectory recognition data, and ensures the quality of moving target trajectory recognition data mining

Tracking Axonal Transports in Time-Lapse Images Obtained from a Microfluidic Culture Platform

In this paper, a procedure is described for tracking moving object trajectories from image sequences acquired from a microfluidic culture platform. Since particles move along the axons, curve structures need to be detected first from the input image sequence. A kymograph analysis technique is applied to detect axon structures from the consolidated image of the input sequence. Horizontally and vertically oriented axons are then detected by applying the process twice to the original and the 90-degree rotated image. Multiple kymographs are generated along the detected axons by projecting image intensity variation through the time-axis. The trajectory detection process is then applied to each kymograph image. To obtain the particle motion information from the entire image sequence, an integration process is applied to each horizontal and vertical kymograph data set. The proposed technique has been applied to image sequences in the present application area. It is demonstrated that practical results can be obtained using time-lapse image sequence data.

SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.