Pointwise Motion Research Articles

Learning motion field of LiDAR point cloud with convolutional networks

This paper proposes a novel method for motion field estimation in two consecutive LiDAR scans with convolutional neural networks (CNNs) wherein object detection, point-wise motion, and object-level motion are learned hierarchically. In the input stage, a unique mapping model serves to describe the LiDAR point cloud and its motion. In the encode network, two input maps are subjected to spatial compression and merged into a correlation layer. In the decode network, the model outputs data encompassing object detection, point-wise motion, and object-level motion. Since existing ground truth datasets are not sufficiently large to train a CNN, we generate a synthetic Vehicle Motion dataset for training. The set contains scenes with several simultaneously moving 3D vehicles recorded by a simulated LiDAR. An experiment conducted on both real and synthetic datasets shows that the proposed method outperforms other state-of-the-art motion field estimation methods.

Walking Ahead: The Headed Social Force Model.

Human motion models are finding an increasing number of novel applications in many different fields, such as building design, computer graphics and robot motion planning. The Social Force Model is one of the most popular alternatives to describe the motion of pedestrians. By resorting to a physical analogy, individuals are assimilated to point-wise particles subject to social forces which drive their dynamics. Such a model implicitly assumes that humans move isotropically. On the contrary, empirical evidence shows that people do have a preferred direction of motion, walking forward most of the time. Lateral motions are observed only in specific circumstances, such as when navigating in overcrowded environments or avoiding unexpected obstacles. In this paper, the Headed Social Force Model is introduced in order to improve the realism of the trajectories generated by the classical Social Force Model. The key feature of the proposed approach is the inclusion of the pedestrians’ heading into the dynamic model used to describe the motion of each individual. The force and torque representing the model inputs are computed as suitable functions of the force terms resulting from the traditional Social Force Model. Moreover, a new force contribution is introduced in order to model the behavior of people walking together as a single group. The proposed model features high versatility, being able to reproduce both the unicycle-like trajectories typical of people moving in open spaces and the point-wise motion patterns occurring in high density scenarios. Extensive numerical simulations show an increased regularity of the resulting trajectories and confirm a general improvement of the model realism.

A Review of Point-Wise Motion Tracking Algorithms for Fetal Magnetic Resonance Imaging.

We review recent feature-based tracking algorithms as applied to fetal magnetic resonance imaging (MRI). Motion in fetal MRI is an active and challenging area of research, but the challenge can be mitigated by strategies related to patient setup, acquisition, reconstruction, and image processing. We focus on fetal motion correction through methods based on tracking algorithms for registration of slices with similar anatomy in multiple volumes. We describe five motion detection algorithms based on corner detection and region-based methods through pseudocodes, illustrating the results of their application to fetal MRI. We compare the performance of these methods on the basis of error in registration and minimum number of feature points required for registration. Harris, a corner detection method, provides similar error when compared to the other methods and has the lowest number of feature points required at that error level. We do not discuss group-wise methods here. Finally, we attempt to communicate the application of available feature extraction methods to fetal MRI.

Pointwise Motion Image (PMI): A Novel Motion Representation and Its Applications to Abnormality Detection and Behavior Recognition

In this paper, we propose a novel motion representation and apply it to abnormality detection and behavior recognition. At first, pointwise correspondences for the foreground in two consecutive video frames are established by performing a salient-region-based pointwise matching algorithm. Then, based on the established pointwise correspondences, a pointwise motion image (PMI) for each frame is built up to represent the motion status of the foreground. The PMI is more suitable for video analysis as it encapsulates a variety of motion information such as pointwise motion speed, pointwise motion orientation, pointwise motion duration, as well as the global shape of the foreground. In addition, it represents all of these pieces of information by a color image in the HSV space, by which many popular techniques in the image processing field can be straightforwardly adopted. By combining the PMI and AdaBoost, a method for abnormality detection and behavior recognition is proposed. The proposed method is shown to possess a high discriminative ability and is capable of dealing with local motion, global motion, and similar motions with different speeds. Experiments including a comparison with two existing methods demonstrate the effectiveness of the proposed representation in abnormality detection and behavior recognition.

Towards pointwise motion tracking in echocardiographic image sequences – Comparing the reliability of different features for speckle tracking

In this paper, we studied the problem of feature-based motion tracking in echocardiographic image sequences. We described the relation between possible feature variations and different kinds of tissue motion using a linear convolution model. We also showed that motion-feature decorrelation (which means that the motion parameters estimated using feature tracking fail to represent the underlying tissue motion) compensation is an ill-posed inverse problem. Instead of finding a method that may provide better compensation results than previous approaches, we used an quantitative measure to compare the reliability of tracking features. Experiment results showed that the use of the reliability measure improved the robustness of displacement estimation. With the help of the reliability measure, we compared the performance of different features using simulations and phantom examples. While we noticed that the radio frequency (RF) signal outperforms the B-mode (BM) signal in the analysis of small deformation (e.g., less than 0.1% compression), we also found out that the BM signal works better than the RF signal in the analysis of large deformation (e.g., larger than 2% compression). The use of a band-passed filtered feature does not result in significant improvement in tracking.

Non-rigid cardiac motion quantification from 2D image sequences based on wavelet synthesis

Abstract Motion quantification from 2D sequential cardiac images has been performed on axial images of the left ventricle (LV) obtained from two different imaging modalities (MRI and Echocardiography images). The detail point wise motion vectors were evaluated by establishing shape correspondence between the consecutive contours after reconstructing curvature information by wavelet synthesis filters at multiple levels. We present a simple approach that optimizes the shape correspondence taking the non-uniform contour variation in to account. The shape matching is done by maximizing the correlation between the approximation coefficient vectors at certain levels. The algorithm has been tested over sets of 2D images and the results are compared with that obtained from a bending energy model. Some experimental results have also been presented for validation of the algorithm.

Minimum-time control for the transport of flexible loads by two manipulators

The minimum-time pointwise motion of two manipulators grasping the same flexible object is examined in this paper. We model the object as a spring with an associated damping factor. The coordinated motion is accomplished through the specification of the trajectory for the mid-point between the two manipulators. The separate trajectories for each manipulator are not relevant per se, since they only need to preserve the desired motion of the mid-point. This freedom in the motion together with the presence of the flexible material causes the minimum time to be less than the minimum time required to move two manipulators with a rigid link between them. This result shows the advantage of exploiting the flexibility of the material being worked by two manipulators.