Use Of Depth Sensors Research Articles

Deep Learning Techniques for Spatiotemporal Weighted Pose Taekwondo Features and Their Application in Tactical Analysis

Taekwondo behavior recognition has become a popular study issue in the past few decades due to its vast range of applications in the visual realm. The research of Taekwondo behavior recognition based on skeleton sequences has received increasing attention in recent years due to the widespread use of depth sensors and the development of real-time skeleton estimate methods based on depth images. In order to characterize the behavioral sequences, the majority of research work currently in existence extracts the spatial domain information of various skeleton joints within frames and the temporal domain information of the skeleton joints between frames. However, this research work ignores the fact that different joints and postures play different roles in determining the behavioral categories. Consequently, this paper presents a spatio-temporal weighted gesture Taekwondo features-based approach for Taekwondo recognition that employs a bilinear classifier to iteratively compute the weights of the static gestures and joint points relative to the action category in order to identify the joint points and gestures with high information content; concurrently, this paper introduces dynamic temporal regularization and Fourier time pyramid algorithms for temporal modeling in order to provide a better temporal analysis of the behavioural features, and ultimately employs support vector machines to complete the behavioural classification. According to experimental results on several datasets, this strategy outperforms certain other methods in terms of recognition accuracy and is highly competitive.

A Survey on Different Approach Used for Sign Language Recognition Using Machine Learning

This paper presents a survey of different approaches used for sign language recognition using machine learning. Sign language recognition is a challenging problem that has attracted considerable research attention in recent years. Various machine learning techniques such as artificial neural networks, support vector machines, decision trees, and convolutional neural networks have been explored to recognize sign language gestures. The survey discusses the strengths and limitations of each approach, as well as their performance on different datasets. Moreover, it also discusses some recent advancements in sign language recognition, including the use of depth sensors and wearable devices. The survey concludes that while machine learning approaches have shown promising results, there is still room for improvement in terms of recognition accuracy, robustness to variations, and real-time performance.

Analyzing the potential of a time-of-flight depth sensor for assembly assistance

Products are becoming more complex and more varied with technological progress. In these circumstances, the assembly process is more prone to errors while training new workers. Adding an assistive system for monitoring the work to give feedback could help to detect errors immediately and to reduce the propagation of errors. Affordable depth sensors that use the time-of-flight technology could be utilized to monitor the assembly process in real-time and to receive immediate feedback. This work evaluates the potential of a depth sensor for assistance in manual assembly. Because these sensors have problems that are difficult to generalize due to external factors such as material properties influencing the measurement, different problems and implications of using these depth sensors for the task were highlighted with a series of experiments with an Azure Kinect, a time-of-flight depth sensor. Rules and restrictions were derived from the results with the goal of improving the use of depth sensors for assistive systems in a manufacturing process. From the experiments and preceding experiences, a couple of rules and restrictions were derived for using depth sensors in that scenario. The results show that although the approach works in general, problems occur in certain conditions, especially due to optical properties of materials for near-infrared wavelengths, such as reflectivity and transmittance, because of the technology of the depth sensor.

Three-dimensional scanning of a figure as the basis for mass customization of industrial clothing collections

The subject of research is clothing development according to individual design chosen by a consumer. The application of the concept of mass customization has become one of the most high-demanded areas of development in light industry. Manufacturers invest heavily in the development of software products, which can help the consumer to choose the right-sized and well-fitting clothes from industrial collections on manufacturers’ websites. While constructing clothing designs on an individual order, and then while manufacturing process under conditions of industrial flow production, exact body dimensions are extremely important. The world light industry is actively using digital technologies in order to measure shapes and determine body features (made-to-measure systems). Such systems make it possible to better take into account the individual characteristics of customers, which allow designing high-quality clothing, characterized by a good fit on a figure of a particular consumer. There is a developed system for obtaining a digital image of a shape, which based on the use of depth sensors. An experimental study of the anthropometric matching of clothes with a figure in a virtual environment was carried out by comparing scanned three-dimensional models of figures with and without clothes. The permissible values of projection gaps were revealed, within them the product visually corresponds to a figure. An approach where there is an information which consists of customers’ preferences, dimensional characteristics, shape and body type in specially created databases, which are used when it is necessary to help to create clothing products that are best suited to the consumer in size and design, is proposed.

Multi-level progressive parallel attention guided salient object detection for RGB-D images

Detecting salient objects in RGB-D images attracts more and more attention in recent years. It benefits from the widespread use of depth sensors and can be applied in the comprehensive understanding of RGB-D images. Existing models focus on double-stream networks which transfer from color stream to depth stream, but depth stream with one channel information cannot learn the same feature as color stream with three channels information even if HHA representation is adopted. In our works, RGB-D four-channels input is chosen, and meanwhile, progressive parallel spatial and channel attention mechanisms are performed to improve feature representation. Spatial and channel attention can pay more attention on partial positions and channels in the image which show higher response to salient objects. Both attentive features are optimized by attentive feature from higher layer, respectively, and parallel fed into recurrent convolutional layer to generate side-output saliency maps guided by saliency map from higher layer. Last multi-level saliency maps are fused together from multi-scale perspective. Experiments on benchmark datasets demonstrate that parallel attention mechanism and progressive optimization operation play an important role in improving the accuracy of salient object detection, and our model outperforms state-of-the-art models in evaluation matrices.

Depth-Sensor Applications for the Elderly: A Viable Option to Promote a Better Quality of Life

This article investigates the use of depth sensors in applications created to preserve the quality of life in older adults. The increase in the numbers in the elderly population, as well as the popularization of structured-light-based sensors, motivate the investigation on how these devices can be used to promote quality of life to the elderly, especially in the prevention of falls. Initially, we present a comprehensive review of the applications of depth sensors in several areas, varying from surveillance and interactive systems to computer-vision applications and three-dimensional (3-D) modeling. Next, we focus on the use of depth sensors in applications to prevent, detect, and address the consequences of falls among the elderly. Our conclusion is that structured-light depth sensors are a viable alternative to develop applications that promote quality of life among the elderly, provided that physical and cognitive issues introduced by aging are considered.