HUMANNET-A Two-Tiered Deep Neural Network Architecture for Self-Occluding Humanoid Pose Reconstruction

Agrast-6: Abridged VGG-Based Reflected Lightweight Architecture for Binary Segmentation of Depth Images Captured by Kinect

Binary object segmentation is a sub-area of semantic segmentation that could be used for a variety of applications. Semantic segmentation models could be applied to solve binary segmentation problems by introducing only two classes, but the models to solve this problem are more complex than actually required. This leads to very long training times, since there are usually tens of millions of parameters to learn in this category of convolutional neural networks (CNNs). This article introduces a novel abridged VGG-16 and SegNet-inspired reflected architecture adapted for binary segmentation tasks. The architecture has 27 times fewer parameters than SegNet but yields 86% segmentation cross-intersection accuracy and 93% binary accuracy. The proposed architecture is evaluated on a large dataset of depth images collected using the Kinect device, achieving an accuracy of 99.25% in human body shape segmentation and 87% in gender recognition tasks.

Prediction and Planning of Sports Competition Based on Deep Neural Network

Physical education curriculum has been paid more and more attention by teachers and parents, and having a healthy body is the foundation. School sports competition is also more and more concerned by major researchers, and scholars have produced in-depth research and analysis of sports competition results prediction because prediction results can better let teachers carry out appropriate sports training for students, so as to achieve the best learning effect. The construction of the prediction model and whether the performance and universality of the model after construction are suitable for predicting sports competitions have also become a major research point. Deep neural network is a complex network method to analyze the structure of the human brain, which plays a core role in the field of sports planning and performance prediction and can know the future performance of athletes or students in advance in sports competitions. This paper establishes the autoregressive summation model prediction model, the complex neural network prediction model, and the improved complex neural network prediction model. It is concluded that only the improved BP neural network model has a remarkable effect on performance prediction, and the prediction value obtained by this prediction model can reduce the systematic error of prediction, so that it can better infer the performance prediction of sports competitions in China and plan which sports events are suitable for which prediction model.

Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images

The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground–background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time.

Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

The analysis and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem of recognition of animal poses, which has numerous applications in zoology, ecology, biology, and entertainment. We propose a methodology to recognize dog poses. The methodology includes the extraction of frames for labeling from videos and deep convolutional neural network (CNN) training for pose recognition. We employ a semi-supervised deep learning model of reinforcement. During training, we used a combination of restricted labeled data and a large amount of unlabeled data. Sequential CNN is also used for feature localization and to find the canine’s motions and posture for spatio-temporal analysis. To detect the canine’s features, we employ image frames to locate the annotations and estimate the dog posture. As a result of this process, we avoid starting from scratch with the feature model and reduce the need for a large dataset. We present the results of experiments on a dataset of more than 5000 images of dogs in different poses. We demonstrated the effectiveness of the proposed methodology for images of canine animals in various poses and behavior. The methodology implemented as a mobile app that can be used for animal tracking.

Reconstruction of a 3D Human Foot Shape Model Based on a Video Stream Using Photogrammetry and Deep Neural Networks

Reconstructed 3D foot models can be used for 3D printing and further manufacturing of individual orthopedic shoes, as well as in medical research and for online shoe shopping. This study presents a technique based on the approach and algorithms of photogrammetry. The presented technique was used to reconstruct a 3D model of the foot shape, including the lower arch, using smartphone images. The technique is based on modern computer vision and artificial intelligence algorithms designed for image processing, obtaining sparse and dense point clouds, depth maps, and a final 3D model. For the segmentation of foot images, the Mask R-CNN neural network was used, which was trained on foot data from a set of 40 people. The obtained accuracy was 97.88%. The result of the study was a high-quality reconstructed 3D model. The standard deviation of linear indicators in length and width was 0.95 mm, with an average creation time of 1 min 35 s recorded. Integration of this technique into the business models of orthopedic enterprises, Internet stores, and medical organizations will allow basic manufacturing and shoe-fitting services to be carried out and will help medical research to be performed via the Internet.

Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

Over the last decade, a driver’s distraction has gained popularity due to its increased significance and high impact on road accidents. Various factors, such as mood disorder, anxiety, nervousness, illness, loud music, and driver’s head rotation, contribute significantly to causing a distraction. Many solutions have been proposed to address this problem; however, various aspects of it are still unresolved. The study proposes novel geometric and spatial scale-invariant features under a boosting framework for detecting a driver’s distraction due to the driver’s head panning. These features are calculated using facial landmark detection algorithms, including the Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). The proposed approach is compared with six existing state-of-the-art approaches using four benchmark datasets, including DrivFace dataset, Boston University (BU) dataset, FT-UMT dataset, and Pointing’04 dataset. The proposed approach outperforms the existing approaches achieving an accuracy of 94.43%, 92.08%, 96.63%, and 83.25% on standard datasets.