Pedestrian Reidentification Based on Feature Fusion and Metric Learning

The pedestrian recognition in public environment is influenced by the pedestrian environment and the dynamic characteristic boundary factors, so it is easy to produce the tracking error. In order to improve the ability of pedestrian re-identification in public environment, we need to carry out feature fusion and metric learning, and propose pedestrian re-identification based on feature fusion and metric learning. The geometric grid area model of pedestrian recognition in public environment is constructed, the method of fuzzy dynamic feature segmentation is used to reconstruct the dynamic boundary feature point of pedestrian recognition in public environment, the method of bottom-up modeling is used to design the dynamic area grid model of pedestrian recognition in public environment, the design of dynamic area grid model is three-dimensional grid area, the grayscale pixel set of pedestrian recognition dynamic constraint under public environment is extracted, the boundary feature fusion is carried out according to the distribution intensity of grayscale, the image fusion and enhancement information processing of pedestrian recognition under public environment, and the method of 3D dynamic constraint is used to realize the local motion planning of pedestrian recognition under public environment, and the recognition feature fusion and learning of pedestrian recognition under public environment is realized according to the result of contour segmentation. The simulation results show that the method is used for pedestrian recognition again in public environment, and the fuzzy judgment ability of pedestrian dynamic edge features is strong, which makes the error controlled below 10 mm, and the fluctuation of pedestrian recognition again is more stable, the recognition accuracy is higher and the robustness is better.

Time-Varying Formation Control for Heterogeneous Planar Underactuated Multi-Vehicle Systems

This paper presents a distributed control approach for time-varying formation of heterogeneous planar underactuated vehicle networks without global position measurements. All vehicles in the network are modeled as generic three degree of freedom planar rigid bodies with two control inputs, and are allowed to have non-identical dynamics. Feasible trajectories are generated for each vehicle using the nonholonomic constraints of the vehicle dynamics. By exploiting the cascaded structure of the planar vehicle model, a transformation is introduced to define the reduced order error dynamics, and then, a sliding-mode control law is proposed. Low level controller for each vehicle is derived such that it only requires relative position and local motion information of its neighbors in a given directed communication network. The proposed formation control law guarantees the uniform global asymptotic stability (UGAS) of the closed-loop system subject to bounded uncertainties and disturbances. The proposed approach can be applied to underactuated vehicle networks consisting of mobile robots, surface vessels and planar aircraft. Simulations are presented to demonstrate the effectiveness of the proposed control scheme.

Development of Task-Space Nonholonomic Motion Planning Algorithm Based on Lie-Algebraic Method

In this paper, a Lie-algebraic nonholonomic motion planning technique, originally designed to work in a configuration space, was extended to plan a motion within a task-space resulting from an output function considered. In both planning spaces, a generalized Campbell–Baker–Hausdorff–Dynkin formula was utilized to transform a motion planning into an inverse kinematic task known for serial manipulators. A complete, general-purpose Lie-algebraic algorithm is provided for a local motion planning of nonholonomic systems with or without output functions. Similarities and differences in motion planning within configuration and task spaces were highlighted. It appears that motion planning in a task-space can simplify a planning task and also gives an opportunity to optimize a motion of nonholonomic systems. Unfortunately, in this planning there is no way to avoid working in a configuration space. The auxiliary objective of the paper is to verify, through simulations, an impact of initial parameters on the efficiency of the planning algorithm, and to provide some hints on how to set the parameters correctly.

Local Dynamics of the Hydration Water and Poly(Methyl Methacrylate) Chains in PMMA Networks

The dynamic behavior of water molecules and polymer chains in a hydrated poly(methyl methacrylate) (PMMA) matrix containing a small amount of water molecules was investigated. Water molecules have been widely recognized as plasticizers for activating the segmental motion of polymer chains owing to their ability to reduce the glass transition temperature. In this study, combined with judicious hydrogen/deuterium labeling, we conducted quasi-elastic neutron scattering (QENS) experiments on PMMA for its dry and hydrated states. Our results clearly indicate that the dynamics of hydrated polymer chains are accelerated, and that individual water molecules are slower than bulk water. It is therefore suggested that the hydration water affects the local motion of PMMA and activates the local relaxation process known as restricted rotation, which is widely accepted to be generally insensitive to changes in the microenvironment.

Disentangling of Local and Wide-Field Motion Adaptation

Motion adaptation has been attributed in flying insects a pivotal functional role in spatial vision based on optic flow. Ongoing motion enhances in the visual pathway the representation of spatial discontinuities, which manifest themselves as velocity discontinuities in the retinal optic flow pattern during translational locomotion. There is evidence for different spatial scales of motion adaptation at the different visual processing stages. Motion adaptation is supposed to take place, on the one hand, on a retinotopic basis at the level of local motion detecting neurons and, on the other hand, at the level of wide-field neurons pooling the output of many of these local motion detectors. So far, local and wide-field adaptation could not be analyzed separately, since conventional motion stimuli jointly affect both adaptive processes. Therefore, we designed a novel stimulus paradigm based on two types of motion stimuli that had the same overall strength but differed in that one led to local motion adaptation while the other did not. We recorded intracellularly the activity of a particular wide-field motion-sensitive neuron, the horizontal system equatorial cell (HSE) in blowflies. The experimental data were interpreted based on a computational model of the visual motion pathway, which included the spatially pooling HSE-cell. By comparing the difference between the recorded and modeled HSE-cell responses induced by the two types of motion adaptation, the major characteristics of local and wide-field adaptation could be pinpointed. Wide-field adaptation could be shown to strongly depend on the activation level of the cell and, thus, on the direction of motion. In contrast, the response gain is reduced by local motion adaptation to a similar extent independent of the direction of motion. This direction-independent adaptation differs fundamentally from the well-known adaptive adjustment of response gain according to the prevailing overall stimulus level that is considered essential for an efficient signal representation by neurons with a limited operating range. Direction-independent adaptation is discussed to result from the joint activity of local motion-sensitive neurons of different preferred directions and to lead to a representation of the local motion direction that is independent of the overall direction of global motion.