The paper is a comprehensive study on classification of motion capture data on the basis of dynamic time warping (DTW) transform. It presents both theoretical description of all applied and newly proposed methods and experimentally obtained results on real dataset of human gait with 436 samples of 30 males. The recognition is carried out by the classical DTW nearest neighbors classifier and introduced DTW minimum distance scheme. Class prototypes are determined on the basis of DTW alignment and chosen methods of averaging rotations represented by Euler angles and unit quaternions. In the basic classification approach the whole pose configuration space is taken into account. The influence of different rotation distance functions operating on Euler angles and unit quaternions, on an obtained accuracy of recognition is investigated. What is more, a differential filtering in time domain which approximates angular velocities and accelerations of subsequent joints is utilized. Because in the case of unit quaternions representing rotations classical subtraction is unworkable, the differential filtering based on a product with a conjugated quaternion is applied. The main contribution of the paper is also related to the proposed and successfully validated approach of an exploration of pose configuration space. It selects the most discriminative joints of a skeleton model in considered classification problem in a binary or fuzzy way. The introduced approach utilizes hill climbing and genetic search strategies as well as DTW transform based evaluation. The selection makes the recognition more efficient and reduces pose signatures.

In this study, a framework for view‐invariant gait recognition on the basis of markerless motion tracking and dynamic time warping (DTW) transform is presented. The system consists of a proposed markerless motion capture system as well as introduced classification method of mocap data. The markerless system estimates the three‐dimensional locations of skeleton driven joints. Such skeleton‐driven point clouds represent poses over time. The authors align point clouds in every pair of frames by calculating the minimal sum of squared distances between the corresponding joints. A point cloud distance measure with temporal context has been utilised in k ‐nearest neighbours algorithm to compare time instants of motion sequences. To enhance the generalisation of the recognition and to shorten the processing time, for every individual a single multidimensional time series among several multidimensional time series describing the individual's gait is established. The correct classification rate has been determined on the basis of a real dataset of human gait. It contains 230 gait cycles of 22 subjects. The tracking results on the basis of markerless motion capture are referenced to Vicon system, whereas the achieved accuracies of recognition are compared with the ones obtained by DTW that is based on rotational data.

This work is focused on the preliminary stage of the 3D drone tracking challenge, namely the precise detection of drones on images obtained from a synchronized multi-camera system. The YOLOv5 deep network with different input resolutions is trained and tested on the basis of real, multimodal data containing synchronized video sequences and precise motion capture data as a ground truth reference. The bounding boxes are determined based on the 3D position and orientation of an asymmetric cross attached to the top of the tracked object with known translation to the object’s center. The arms of the cross are identified by the markers registered by motion capture acquisition. Besides the classical mean average precision (mAP), a measure more adequate in the evaluation of detection performance in 3D tracking is proposed, namely the average distance between the centroids of matched references and detected drones, including false positive and false negative ratios. Moreover, the videos generated in the AirSim simulation platform were taken into account in both the training and testing stages.

Introduction: It is proved that there are differences between gait performed by females and males, which appear in movements of selected body parts. Despite numerous state-of-the-art studies related to the discriminative analysis of motion capture data, the question of whether measures of signal complexity and uncertainty can extract valuable features for the problem of sex distinction still remains open. It is the subject of the paper.Methods: Correlation dimension, as well as approximate and sample entropies, are selected to describe motion data. In the numerical experiments, the collected dataset with 884 samples of 25 females and 30 males was used. The measurements took place in the Human Motion Laboratory (HML), equipped with a highly precise motion capture system. Two variants of data representation were investigated-time series that contain joint rotations of taken skeleton model as well as positions of the markers attached to the human body. Finally, a comparative analysis between the populations of females and males using descriptive statistics, non-parametric estimation, and statistical hypotheses verification was carried out.Results: There are statistically significant sex differences extracted by the taken measures. In general, the movements of lower limbs result in greater values of correlation dimension and entropies for females, while selected upper body parts play a similar role for males. The dissimilarities are mainly observed in hip, ankle, shoulder, and head movements.Discussion: Correlation dimension and entropy measures provide robust and explainable features of motion capture data with a valuable description of the human locomotion system. Thus, beyond the importance of discovered differences between females and males, their interpretation and understanding are also known.

The subject of the paper is a multimodal dataset (DPJAIT) containing drone flights prepared in two variants – simulation-based and with real measurements captured by the gold standard Vicon system. It contains video sequences registered by the synchronized and calibrated multicamera set as well as reference 3D drone positions in successive time instants obtained from simulation procedure or using the motion capture technique. Moreover, there are scenarios with ArUco markers in the scene with known 3D positions and RGB cameras mounted on drones for which internal parameters are given. Three applications of 3D tracking are demonstrated. They are based on the overdetermined set of linear equations describing camera projection, particle swarm optimization, and the determination of the extrinsic matrix of the camera attached to the drone utilizing recognized ArUco markers.