Motion Trajectory Recognition Research Articles

Application of motion trajectory recognition based on remote sensing image optical processing in optimizing swimming training schemes

Application of motion trajectory recognition based on remote sensing image optical processing in optimizing swimming training schemes

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.

Intelligent recognition of dance training movements based on machine learning and embedded system

Recognizing human movement is an important research topic in the field of human-computer interaction, and people expect it to be used in smart homes, virtual reality, and electronic games. Based on the interaction between humans and computers, more and more attention has been paid, especially in the field of smart home action recognition. Through observation, people can understand the intention of intelligent interaction is included in the main part. However, the current recognition algorithms still cannot meet the actual requirements of the accuracy, real-time and robustness of human motion recognition. Especially in order to recognize complex human movements in real time, it is imperative to solve several problems in motion capture and recognition. Establishing the feature parameter angle of the feature vector space of motion data, using the pre-recognition algorithm is based on multi-class support vector machines. The motion recognition algorithm takes advantage of the accurate and fast classification function of svm. Based on the structural differences of the motion data, most of the data can be correctly identified. The optimal motion recognition algorithm uses hmm to correct the svm error recognition result through the random constraint relationship between the error recognition data and the actual label. Based on data simulation and analysis, each variable determined by the grid search algorithm has the highest accuracy in the optimization of each variable of the support vector machine. Finally, a smart home simulation experiment interactive system was built, and a local database was created, including 1,300 processes. The real-time algorithm uses the data in the local database for training and testing. Experimental results show that the motion recognition algorithm in this paper improves the accuracy and robustness of complex motion recognition. While meeting the real-time recognition conditions, the correct answer rate of the final operation can reach 9.6%. The human motion trajectory recognition system uses the three-dimensional trajectory of gestures to recognize motion. The information in the three-dimensional space is more comprehensive, and the orbit recognition is more robust.

Describing Local Reference Frames for 3-D Motion Trajectory Recognition

Motion trajectories tracked from the points of interest can provide the key relevant features for characterizing the motion patterns in video. As the increasing number of 3-D vision sensors rises, the 3-D motion trajectories that serve as motion representations have been applied successfully to video retrieval and analysis, scene understanding, motion recognition, and so on, in existing works. Most of these works use raw data of motion trajectories directly or draw simple geometric quantities to describe the motion trajectories, whereas these simple descriptions are not intrinsically complete as they cannot feature the orientation changes of moving points along the 3-D motion trajectories. In principle, orientation changes of a single moving point in 3-D space have to been obtained by resorting to high-order derivatives, but the high-order derivatives would result in high sensitivity to noise. This paper tackles the problem by describing the local reference frames along 3-D motion trajectories, while we consider a motion trajectory as a temporal sequence of local reference frames. The maximal blurred segment of the noisy discrete curves is employed to estimate the local reference frames without high-order derivatives involved, and the local reference frame contains complete information of positions and orientations in the 3-D Euclidean space. To describe such local reference frames, we use the rotations and local square root velocities of local reference frames as the proposed descriptor to characterize the position and orientation changes of the moving points along the motion trajectories. In the experiments, we evaluate the effectiveness of the proposed descriptor by applying it to the gesture recognition on two large benchmark data sets that contain hand motion trajectories. The results show that our proposed descriptor can achieve superior performance compared to the existing descriptors and state-of-the-art methods in the 3-D motion trajectory recognition.

On Multiscale Self-Similarities Description for Effective Three-Dimensional/Six-Dimensional Motion Trajectory Recognition

Motion trajectories provide compact informative clues in characterizing motion behaviors of human bodies, robots, and moving objects. This paper devises an invariant and unified descriptor for three-dimensional/six-dimensional (3-D/6-D) motion trajectories recognition by exploring the latent motion patterns in the multiscale self-similarity matrices (MSM) within a motion trajectory and its components. The MSM approach transforms a motion trajectory in Euclidean space into a set of similarity matrices and exhibits strong invariances, in which each matrix can be regarded as a grayscale image. Next, the histograms of oriented gradients features extracted from the MSM representation are concatenated as the final trajectory descriptor. In addition, an improved kernel MSM is raised by calculating the pairwise kernel distances. Finally, extensive 3-D/6-D motion trajectory recognition experiments on three public datasets with a linear support vector machine classifier are conducted to verify the effectiveness and efficiency of the proposed approach.

Human action recognition based on point context tensor shape descriptor

Motion trajectory recognition is one of the most important means to determine the identity of a moving object. A compact and discriminative feature representation method can improve the trajectory recognition accuracy. This paper presents an efficient framework for action recognition using a three-dimensional skeleton kinematic joint model. First, we put forward a rotation-scale-translation-invariant shape descriptor based on point context (PC) and the normal vector of hypersurface to jointly characterize local motion and shape information. Meanwhile, an algorithm for extracting the key trajectory based on the confidence coefficient is proposed to reduce the randomness and computational complexity. Second, to decrease the eigenvalue decomposition time complexity, a tensor shape descriptor (TSD) based on PC that can globally capture the spatial layout and temporal order to preserve the spatial information of each frame is proposed. Then, a multilinear projection process is achieved by tensor dynamic time warping to map the TSD to a low-dimensional tensor subspace of the same size. Experimental results show that the proposed shape descriptor is effective and feasible, and the proposed approach obtains considerable performance improvement over the state-of-the-art approaches with respect to accuracy on a public action dataset.

Adaptive 3D shape context representation for motion trajectory classification

The measurement of similarity between two motion trajectories is one of the fundamental task for motion analysis, perception and recognition. Previous research focus on 2D trajectory similarity measurement. With the advent of 3D sensors, it is possible to collect large amounts of 3D trajectory data for more precise motion representation. As trajectories in 3D space may often exhibit a similar motion pattern but may differ in location, orientation, scale, and appearance variations, the trajectory descriptor must be invariant to these degrees of freedom. Shape context is one of the rich local shape descriptors can be used to represent the trajectory in 2D space, however, rarely applied in the 3D motion trajectory recognition field. To handle 3D data, in this paper, we first naturally extend the shape context into the spatiotemporal domain by adopting a spherical neighborhood, and named it 3D Shape Context(3DSC). To achieve better global invariant on trajectories classification, the adaptive outer radius of 3DSC for extracting 3D Shape Context feature is proposed. The advantages of our proposed 3D shape context are: (1) It is invariant to motion trajectories translation and scale in the spatiotemporal domain; (2) It contains the whole trajectory points in the 3DSC ball volume, thus can achieve global information representation and is good for solving sub-trajectories problem; (3) It is insensitive to the appearance variations in the identical meaning trajectories, meanwhile, can greatly discriminate the distinct meaning trajectories. In trajectory recognition phase, we consider a feature-to-feature alignment between motion trajectories based on dynamic time warping and then use the one nearest neighbor (1NN) classifier for final accuracy evaluation. We test the performance of proposed 3D SC-DTW on UCI ASL large dataset, Digital hand dataset and the experimental results demonstrate the effectiveness of our method.

Point Context: An Effective Shape Descriptor for RST-Invariant Trajectory Recognition

Motion trajectory recognition is important for characterizing the moving property of an object. The speed and accuracy of trajectory recognition rely on a compact and discriminative feature representation, and the situations of varying rotation, scaling and translation has to be specially considered. In this paper we propose a novel feature extraction method for trajectories. Firstly a trajectory is represented by a proposed point context, which is a rotation-scale-translation (RST) invariant shape descriptor with a flexible tradeoff between computational complexity and discrimination, yet we prove that it is a complete shape descriptor. Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space. Experimental results show that, the proposed trajectory feature shows encouraging improvement than state-of-art methods.

On Signature Invariants for Effective Motion Trajectory Recognition

Motion trajectory can be an informative and descriptive clue that is suitable for the characterization of motion. Studying motion trajectory for effective motion description and recognition is important in many applications. For instance, motion trajectory can play an important role in the representation, recognition and learning of most long-term human or robot actions, behaviors and activities. However, effective trajectory descriptors are lacking and most reported work just uses motion trajectory in its raw data form. In this paper, we propose a novel motion trajectory signature descriptor and study its rich descriptive invariants which benefit effective motion trajectory recognition. These invariants are key measures of the flexibility and effectiveness of a descriptor. Substantial descriptive invariants can be deduced from the proposed trajectory signature, which is attributed to the computational locality of the signature components. We first present the signature definition and its robust implementation. Then the signature's invariants are elaborated. A non-linear inter-signature matching algorithm is developed to measure the signature's similarity for trajectory recognition. Experiments are conducted to recognize human sign language, in which both synthetic and real data are used to verify the signature's invariants, and to illustrate the effectiveness in the signature recognition.