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Predicting human motion based on past observed motion is one of the challenging issues in computer vision and graphics. Existing research works are dealing with this issue by using discriminative models and showing the results for cases that follow a homogeneous distribution (in distribution) and not discussing the issues of the domain shift proble...
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In this study, we address the challenge of 3D human motion prediction from motion capture data, which has become critical in various applications such as autonomous vehicles and human-robot interaction. Previous deep learning-based methods have improved prediction accuracy, but require significant network parameters and do not effectively consider...
Human motion prediction aims to forecast future human poses given a historical motion. Current state-of-the-art approaches rely on deep learning architectures of arbitrary complexity, such as Recurrent Neural Networks (RNN), Graph Convolutional Networks (GCN), and typically requires multiple training stages and more parameters. In addition, existin...
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... With the problem of computational resources being solved by introducing high-performance graphical processing units, Deep learning architectures impact dynamic motion prediction, including many other similar areas. Recent deep recurrent neural network-based methods generate expected future poses step by step [24,25]. Authors [21] use generative adversarial networks architecture to predict the human motions from a 3D skeleton pose. ...
In the modern-day scenario, machines and humans are expected to work together and collaborate in several social and manufacturing environments. The machines should predict humans’ next move for effective collaborations by observing their present move. Human motion modelling and prediction are fundamental and challenging problems involving computer vision and graphics. To help solve some of the challenges, in the present investigation, we propose an innovative idea of developing a new cost function as the objective function based on adaptive sampling, which is subsequently used with an ’Adam’ optimizer for training and validating a specially configured Deep Learning architecture. Our proposed development produced significantly improved results regarding future pose estimation/predictions. The adaptiveness of the proposed cost function is based on a bell-shaped locally weighted function. It has been observed that the area covered by the cost function plays a vital role during training, and the bell-shaped function’s width helps decide the region of importance for the training samples. The proposed cost function has been used for training a gated recurrent unit (GRU) based encoder-decoder architecture. The encoder takes the observed input sequences, extracts the input sequence’s significant variability, and passes it to the decoder. The decoder takes it as input, trains using the adaptive sampling-based method, and predicts future poses. We have experimented with this function in various sizes and shapes and compared the results obtained with some state-of-the-art research results. As elaborated in this paper, we obtained much-improved results in almost all the cases.
... Due to the advantages of DCT in encoding temporal information, many recent GCN methods [31,38,40] use DCT to encode pose trajectories. DCT can preserve temporal smoothness well but does not capture enough features of the trajectory space to determine the trend of motion sequences, instantaneous changes, and other deep-level detail information. ...
Human motion prediction in various motion capture applications, e.g., optical and inertial, is challenging because of the complexity of human motion sequences. Current studies on this issue have insufficient analysis on the latent motion information in a given motion sequence, such as motion trends, transient changes, and temporal evolution. Meanwhile, methods using simple graph convolution networks suffer from over-smoothing, causing the predicted poses staying invariant in long-term prediction. To address these challenges, we propose a multi-feature-based orthogonal graph convolution network (MFOGCN), where the multi-feature extraction consists of two key modules: (1) hybrid spectral transform, which captures local transient features and global motion trends of motion sequences by discrete wavelet transform while considering temporal smoothing between human joints and (2) mask-aware multiple attention, with sliding time windows to extract motion sequence feature representations from historical multiple subsequences, refining the correlation between adjacent poses while obtaining global dependencies between sequences. In addition, we propose orthogonal graph convolution and orthogonal loss for the prediction network, which help to stabilize the feature transformation of the graph convolution to resolve the over-smoothing issue. An extensive evaluation on the Human 3.6M, AMASS and 3DPW datasets has been conducted, showing reliable effectiveness of the proposed MFOGCN that outperforms other approaches.
... Backpropagation employs methods such as gradient descent to minimise the cost function, which represents the discrepancy between the predicted and observed values. The adornments serve as evident indications of practical knowledge acquisition [4,5]. ...
There is increasing research into the potential benefits of incorporating artificial intelligence (AI) and machine learning algorithms into emergency medical services. AI is finding new applications across a wide range of sectors, one of which is healthcare, where it is being used to enhance clinical diagnostics. AI solutions have enormous untapped potential to improve healthcare efficiency and quality, thus researchers have focused heavily on emergency medicine (EM). Many individuals without prior experience with any physician often receive their initial medical care in the emergency room. Two areas that could benefit from the implementation of AI are reducing waiting times and enhancing diagnostic capabilities. This study provides further explanation of how AI is used in emergency rooms. Several machine learning‐based algorithms are also addressed. In this research, we summarise recent developments in the use of AI in EM. This research tries to summarise the usefulness of AI in EM by looking at recent developments in emergency department operations and clinical patient management.
... Human motion prediction, as shown in Fig.1, has been an active research area in computer vision in the past few years, holding great potential for real-world applications such as autonomous driving [1], activity recognition, target tracking [2], and humanrobot collaboration [3,4], etc. Accurately predicting future motion is a challenging task for classical physical models [5] due to the uncertainty generated by the complexity of human kinematics and the physical environment. The success of deep learning and the emergence of large-scale motion capture datasets have facilitated data-driven research in this field [6][7][8][9][10][11][12][13][14][15][16][17][18]. The human motion sequence can be naturally represented as a collection of joint trajectories equipped with complex spatial-temporal dependencies. ...
Human motion prediction is a challenging task in human-centric computer vision that involves forecasting future poses based on historical sequences. Despite recent progress in modeling spatial-temporal relationships of motion sequences using complex structured graphs, few approaches have been able to provide an adaptive and compact representation for varying graph structures of human motion. Inspired by the advantages of MLP-Mixer, a lightweight architecture developed for learning complex interactions in multi-dimensional data, we explore its potential as a backbone for motion prediction. Human motion prediction is a challenging task in human-centric computer vision, involving forecasting future poses based on historical sequences. Despite recent progress in modeling spatial-temporal relationships of motion sequences using complex structured graphs, few approaches have provided an adaptive and lightweight representation for varying graph structures of human motion. Taking inspiration from the advantages of MLP-Mixer, a lightweight architecture designed for learning complex interactions in multi-dimensional data, we explore its potential as a backbone for motion prediction. To this end, we propose a novel MLP-Mixer-based adaptive spatial-temporal pattern learning framework (M\(^2\)AST). Our framework includes an adaptive spatial mixer to model the spatial relationships between joints, an adaptive temporal mixer to learn temporal smoothness, and a local dynamic mixer to capture fine-grained cross-dependencies between joints of adjacent poses. The final method achieves a compact representation of human motion dynamics by adaptively considering spatial-temporal dependencies from coarse to fine. Unlike the trivial spatial-temporal MLP-Mixer, our proposed approach can more effectively capture both local and global spatial-temporal relationships simultaneously. We extensively evaluated our proposed framework on three commonly used benchmarks (Human3.6M, AMASS, 3DPW MoCap), demonstrating comparable or better performance than existing state-of-the-art methods in both short and long-term predictions, despite having significantly fewer parameters. Overall, our proposed framework provides a novel and efficient solution for human motion prediction with adaptive graph learning.
