Motion Prediction Method Research Articles

Predictive local receptive fields based respiratory motion tracking for motion-adaptive radiotherapy.

Extracranial robotic radiotherapy employs external markers and a correlation model to trace the tumor motion caused by the respiration. The real-time tracking of tumor motion however requires a prediction model to compensate the latencies induced by the software (image data acquisition and processing) and hardware (mechanical and kinematic) limitations of the treatment system. A new prediction algorithm based on local receptive fields extreme learning machines (pLRF-ELM) is proposed for respiratory motion prediction. All the existing respiratory motion prediction methods model the non-stationary respiratory motion traces directly to predict the future values. Unlike these existing methods, the pLRF-ELM performs prediction by modeling the higher-level features obtained by mapping the raw respiratory motion into the random feature space of ELM instead of directly modeling the raw respiratory motion. The developed method is evaluated using the dataset acquired from 31 patients for two horizons in-line with the latencies of treatment systems like CyberKnife. Results showed that pLRF-ELM is superior to that of existing prediction methods. Results further highlight that the abstracted higher-level features are suitable to approximate the nonlinear and non-stationary characteristics of respiratory motion for accurate prediction.

Prediction of ship deck inclination angle

The paper focuses on a ship motion prediction method. Unlike the other approaches, this prediction method with extended prediction duration uses dynamic models of the ship and disturbances. Identification procedure is used to determine the current parameters of ship motion dynamic model, and disturbance model is adjusted for certain motion conditions. Modeling results with the analysis are given, and the prediction of ship roll and pitch angles by the data obtained at the tests of a heavy aircraft carrier.

The impact of motion prediction methods on surrogate safety analysis: A case study of left-turn and opposite-direction interactions at a signalized intersection in Montreal

ABSTRACTSurrogate safety analysis is a proactive approach that relies on the observation of traffic events without a collision. To measure the proximity to collision, several safety indicators have been proposed in the literature. Most indicators like time to collision (TTC) rely on the choice of a motion prediction method such as the rarely justified prediction at constant velocity. Moreover, most current interpretations of continuous safety indicators use only a single value to qualify the whole interaction, for example, the minimum TTC. The objective of this article is to investigate the impact of motion prediction methods on safety indicators and the interpretation methods of these indicators for safety diagnosis. A framework is proposed to predict road users’ future positions depending on different extrapolation hypotheses: kinematic methods such as constant velocity and motion pattern matching learnt from the observed trajectories. Two interpretation approaches of safety indicators are presented: (a) aggregated safety indicator distributions and (b) indicator profile classification. The framework is applied to the safety diagnosis of left-turn and opposite-direction interactions at a signalized intersection in Montreal, Canada. The results show that motion pattern matching is able to compute the safety indicators earlier and to provide a larger number of measurements.

Exposure of Population and Energy-Related Base Facilities to Shaking Intensity Predicted for Nankai Megathrust Earthquakes

The exposure of the area and population as well as energy-related base facilities to the shaking intensity predicted for the anticipated megathrust earthquakes along the Nankai Trough subduction zones is evaluated. First, area and population exposure is evaluated considering six cases of shaking intensity distribution calculated using strong motion prediction methods and an attenuation formula. Next, the exposure of energy-related base facilities is evaluated for power generation plants, oil refineries, and LNG terminals in terms of their capacities for power generation, oil refining, and storage, respectively. Exposed capacities can be evaluated approximately for arbitrary levels of shaking intensity, providing fundamental information on the potential reduction of energy supply capabilities in an earthquake disaster.

Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization

Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events (M>7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.

Online adaptive multiple pedestrian tracking in monocular surveillance video

Automatic online multiple pedestrian tracking is a rather important and challenging task in the field of machine vision. A new multiple pedestrian tracking system is proposed in this paper, which combines pedestrian detection, motion prediction, target matching and adaptive location adjustment methods. The clip-split strategy was adopted for optimization of the detected pedestrian candidates, which resulted in great improvement of the tracking accuracies, especially when the marginal areas of the detected target candidates contained background scenes. For each frame, the proposed adaptive location adjustment method was used to adjust the location and scale of the targets to deal with drifting problems where necessary, especially after severe occlusions. Experimental results on three challenging real-world datasets demonstrated that the proposed tracker has excellent performance over other state-of-the-art trackers based on MOT metrics.

A simulation based method for vehicle motion prediction

The movement of a vehicle is much affected by surrounding environments such as road shapes and other traffic participants. This paper proposes a new vehicle motion prediction method to predict future motion of an on-road vehicle which is observed by a stereo camera system mounted on a moving vehicle. Our proposed algorithm considers not only the history movement of the observed vehicle, but also the environment configuration around the vehicle. To find feasible paths under a dynamic road environment, the Rapidly-Exploring Random Tree (RRT) is used. A simulation based method is then applied to generate future trajectories by combining results from RRT and a motion prediction algorithm modelled as a Gaussian Mixture Model (GMM). Our experiments show that our approach can predict future motion of a vehicle accurately, and outperforms previous works where only motion history is considered for motion prediction.

BRVO: Predicting pedestrian trajectories using velocity-space reasoning

We introduce a novel, online method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human–robot interaction and collision-free navigation. Our formulation uses velocity obstacles to model the trajectory of each moving pedestrian in a robot’s environment and improves the motion model by adaptively learning relevant parameters based on sensor data. The resulting motion model for each agent is computed using statistical inferencing techniques, including a combination of ensemble Kalman filters and a maximum-likelihood estimation algorithm. This allows a robot to learn individual motion parameters for every agent in the scene at interactive rates. We highlight the performance of our motion prediction method in real-world crowded scenarios, compare its performance with prior techniques, and demonstrate the improved accuracy of the predicted trajectories. We also adapt our approach for collision-free robot navigation among pedestrians based on noisy data and highlight the results in our simulator.

递推式视频前景分割方法

This paper presents a kind of recursive video object segmentation method. Previous video cutout methods present three major limitations especially for complex video scene: firstly they lack the ability to deal with the inseparable color problem between foreground and background scenes, and the occlusion/disocclusion problem caused by large movement, new exposed regions or topology change are also difficult to be solved. Lastly, the consideration on how to build the color model for the following probability estimation, which plays a critical role in the final result, has always been ignored in most of existing methods. In our method, all above limitations are taken full consideration. A kind of motion prediction method based on local coherence is adopted to separate the inseparable color. Then a self-adaptive extended sampling method is used to repair the video discontinuity caused by occlusion/disocclusion problem, and also we built the color model by sampling all the pixels from selected regions in order to make it clean and representative. Lastly, the final segmentation result is generated by using 3D Graph Cut algorithm according to the spatio-temporal coherence of the video. The experimental results are presented to demonstrate the effectiveness of the proposed method at achieving high quality results, as well as the robustness of the proposed method against several challenging test inputs.

SIFT flow for large-displacement object tracking.

Traditional tracking methods place an emphasis on how to cope with the variations in target appearance effectively. However, when the motion displacement of the target between image frames becomes larger, these methods may be unstable. This paper presents a novel (to our knowledge) visual object tracking method. In this method, we first introduce scale-invariant feature transform (SIFT) flow into the tracking problem and develop a real-time motion prediction method to capture large displacement between consecutive image frames. Then we use belief propagation (BP) to convert the problem of finding maximum a posteriori probability (MAP) to globally minimizing an energy function to get the best matching pairs of points for producing good candidate regions of the target. And last, the refined point trajectories are obtained according to the bidirectional flow field consistency estimation and covariance region descriptor matching, which can update model states efficiently so as to achieve enhanced robustness for visual tracking. Compared with the state-of-art tracking methods, the experimental results demonstrate that the proposed algorithm shows favorable performance when the object undergoes large motion displacement between image frames.

Novel Motion and Disparity Prediction for Multi-view Video Coding

This paper presents an efficient motion and disparity prediction method for multi-view video coding based on the high efficient video coding (HEVC) standard. The proposed method exploits inter-view candidates for effective prediction of the motion or disparity vector to be coded. The inter-view candidates include not only the motion vectors of adjacent views, but also global disparities across views. The motion vectors coded earlier in an adjacent view were found to be helpful in predicting the current motion vector to reduce the number of bits used in the motion vector information. In addition, the proposed disparity prediction using the global disparity method was found to be effective for interview predictions. A multi-view version based on HEVC was used to evaluate the proposed algorithm, and the proposed correspondence prediction method was implemented on a multi-view platform based on HEVC. The proposed algorithm yielded a coding gain of approximately 2.9% in a high efficiency configuration random access mode.

A Video Compression Coding Algorithm for Network Content Transmission

Video compression coding technology is the key to implement network video transmission, and the increasing requirements of network content transmission quality accelerate the development of video compression coding technology. H.264 is a highly compressed digital video codec standard, and is a hybrid coding mode of differential pulse code modulation (DPCM) and transform encoding. In this paper, we introduced several techniques of hybrid coding algorithm based on H.264 standard, including differential coding, block matching motion prediction method, normalization criteria, variable-size block motion compensation, and multiple hypothesis motion compensation. We did extensive experiments on variable-size block motion compensation coding method. The experimental results show that, with the premise of the 1/4 pixel motion compensation accuracy and hybrid coding, 8 × 8 or larger block partition of content for normal video object, can further make use of space correlation and improve compression efficiency.

Second Order Volterra Series Model Based Fast Least Square Method for Heart Motion Prediction

心脏手术辅助机器人动态地消除心脏表面手术点与手术工具之间的相对运动，帮助医生在心脏不停跳的情况下完成手术并且极大减小病人术后并发症的影响。由于心脏运动的高带宽、非平稳、非线性的特点，机器人很难快速准确跟踪心脏运动。针对这一问题，该文首先通过双谱分析了解到心脏信号具有明显的源于心跳与呼吸运动相互耦合的非线性特征。接着提出基于非线性二阶伏特拉(Volterra)级数模型心脏运动快速预测算法，为控制算法提供参考信号的超前预测值。采用非线性模型描述心脏运动更加精确地表达了心脏运动的物理本质，快速最小二乘算法保证了系统的实时性。对比实验结果表明，非线性预测算法在均方误差方面优于以往的线性预测算法，大大减小了预测误差，提高了手术机器人相对运动消除能力。

An Application of the Improved Autoregressive Method for Two Vessels Roll Motion Prediction

It is of great significance and value to predict the roll of two ships, especially when one is replenishing for the other or in some other conditions. Autoregressive time series analysis method (AR) with Recursive least squares (RLS) theory is the mainstream currently and the effectiveness for the prediction of the motion attitudes have been fully validated. However, there are some differences between the prediction for the motion of two vessels and for that of one. The hydrodynamic interactions between two ships should be taken into consideration and be reflected in the application of the method. In order to solve this issue, this work firstly proposed a double ships autoregressive (DAR) method, which can determine the orders and the parameters of model in real-time with consideration of the interference between two ships and the DAR method was applied to forecast the roll motion between two ships. The simulative results of DAR method show the validity and veracity compared to real value and the advancement compared to the autoregressive (AR) method for single ship motion prediction.

2A1-T03 剣道エージェント:"I"s : 第2報:攻撃動作同定手法(人間機械協調)

In this article, we propose a method of human motion identification for Kendo agent. There are three basic attack motion, "Kote-Uchi", "Men-Uchi" and "Dou-Uchi", in Kendo. Although we proposed attack motion prediction method for kendo agent, the agent can not predict his opponent's motion without selection of motion model, which is generated as Gaussian Mixture Model (GMM). Therefore the agent should choose the suitable model to predict the motion. In order to choose a suitable model, current opponent's motion is identified from current his opponent's state and GMMs. The motion identification method is evaluated by carrying out some experiments.

A hybrid dynamic motion prediction method for multibody digital human models based on a motion database and motion knowledge

In this paper, we present a novel method to predict human motion, seeking to combine the advantages of both data-based and knowledge-based motion prediction methods. Our method relies on a database of captured motions for reference and introduces knowledge in the prediction in the form of a motion control law, which is followed while resembling the actually performed reference motion. The prediction is carried out by solving an optimization problem in which the following conditions are imposed to the motion: must fulfill the goals of the task; resemble the reference motion selected from the database; follow a knowledge-based dynamic motion control law; and ensure the dynamic equilibrium of the human model, considering its interactions with the environment. In this work, we apply the proposed method to a database of clutch pedal depression motions, and we present the results for three predictions. The method is validated by comparing the results of the prediction to motions actually performed in similar conditions. The predicted motions closely resemble the motions in the validation database and no significant differences have been noted either in the motion’s kinematics or in the motion’s dynamics.

A Hybrid Stereoscopic Video Coding Scheme Based on MPEG-2 and HEVC for 3DTV Services

Recently, 3DTV has drawn much attention as a new broadcasting service. In spite of technical advances in 3DTV broadcasting services, there are two barriers that hinder the new services from being launched for terrestrial broadcasting: the lack of available bandwidth for transmission of additional-view video via terrestrial channel and backward compatibility with the legacy 2-D HDTV services. As an alternative, a hybrid stereoscopic video coding scheme is proposed for a stereoscopic 3DTV service, where one-view video is transmitted through the legacy broadcasting systems and the other one is delivered via broadband networks to which TV terminals are connected. In this paper, the proposed hybrid stereoscopic video coding scheme incorporates an MPEG-2 codec for backward compatibility with a legacy 2-D HDTV service and an HEVC codec with inter-view prediction coding as an extension to encode additional-view video sequences with high coding efficiency. The proposed inter-view prediction coding scheme in the extended HEVC incorporates an advanced motion and disparity vector prediction (AMDVP) method for enhanced motion- and disparity-compensated coding. The experimental results demonstrate that the proposed hybrid stereoscopic video coding scheme achieves an average coding efficiency of 38.22% (32.86%) in BD-rate or 1.394 dB (1.235 dB) in BD-PSNR for the out-band (in-band) scenario.

A comparison between optimization-based human motion prediction methods: data-based, knowledge-based and hybrid approaches

In this paper an optimization-based hybrid dynamic motion prediction method is presented. The method is hybrid as the prediction relies both on actually performed motions for reference (following a data-based approach) and on the definition of appropriate performance measures (following a knowledge-based approach). The prediction is carried out through the definition of a constrained non-linear optimization problem, in which the objective function is composed of a weighted combination of data-based and knowledge-based contributions. The weights of each contribution are varied in order to generate a battery of hybrid predictions, which range from purely data-based to purely knowledge-based. The results of the predictions are analyzed and compared against actually performed motions both qualitatively and quantitatively, using a measure of realism defined as the distance of the predicted motions from the mean of the actually performed motions. The method is applied to clutch pedal depression motions and the comparison between the different approaches favors the hybrid solution, which seems to combine the strengths of both data- and knowledge-based approaches, enhancing the realism of the predicted motion.

Evaluating and comparing algorithms for respiratory motion prediction

In robotic radiosurgery, it is necessary to compensate for systematic latencies arising from target tracking and mechanical constraints. This compensation is usually achieved by means of an algorithm which computes the future target position. In most scientific works on respiratory motion prediction, only one or two algorithms are evaluated on a limited amount of very short motion traces. The purpose of this work is to gain more insight into the real world capabilities of respiratory motion prediction methods by evaluating many algorithms on an unprecedented amount of data. We have evaluated six algorithms, the normalized least mean squares (nLMS), recursive least squares (RLS), multi-step linear methods (MULIN), wavelet-based multiscale autoregression (wLMS), extended Kalman filtering, and ε-support vector regression (SVRpred) methods, on an extensive database of 304 respiratory motion traces. The traces were collected during treatment with the CyberKnife (Accuray, Inc., Sunnyvale, CA, USA) and feature an average length of 71 min. Evaluation was done using a graphical prediction toolkit, which is available to the general public, as is the data we used. The experiments show that the nLMS algorithm—which is one of the algorithms currently used in the CyberKnife—is outperformed by all other methods. This is especially true in the case of the wLMS, the SVRpred, and the MULIN algorithms, which perform much better. The nLMS algorithm produces a relative root mean square (RMS) error of 75% or less (i.e., a reduction in error of 25% or more when compared to not doing prediction) in only 38% of the test cases, whereas the MULIN and SVRpred methods reach this level in more than 77%, the wLMS algorithm in more than 84% of the test cases. Our work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation. Additionally, we have seen that the structure of a patient’s respiratory motion trace has strong influence on the outcome of prediction. Further work is needed to determine a priori the suitability of an individual’s respiratory behaviour to motion prediction.

Motion Prediction Methods for Surrogate Safety Analysis

Despite the rise in interest in surrogate safety analysis, little work has been done to understand and test the impact of methods for motion prediction, which are needed to identify whether two road users are on a collision course, and to compute several surrogate safety indicators such as the time to collision. The default, unjustified method used in much of the literature is prediction at constant velocity. In this study, a generic framework is presented to predict road users' future positions depending on their current position and their choice of acceleration and direction. This method results in the possibility of generating many predicted trajectories by sampling distributions of acceleration and direction. Three safety indicators—the time to collision, an extended version of predicted post encroachment time, and a new indicator measuring the probability that the road user's attempted evasive actions will fail to avoid the collision—are computed over all predicted trajectories. These methods and indicators are illustrated in four case studies of lateral road user interactions. The evidence suggests that the prediction method based on the use of a set of initial positions seems to be the most robust. Another contribution of this study is to make all the data and code used available (the code as open source) to enable reproducibility and to start a collaborative effort to compare and improve the methods for surrogate safety analysis.