Markerless Vision-Based Skeleton Tracking in Therapy of Gross Motor Skill Disorders in Children

This chapter presents a research towards implementation of a computer vision system for markerless skeleton tracking in therapy of gross motor skill disorders in children suffering from mild cognitive impairment. The proposed system is based on a low-cost 3D sensor and a skeleton tracking software. The envisioned architecture is scalable in the sense that the system may be used as a stand-alone assistive tool for tracking the effects of therapy or it may be integrated with an advanced autonomous conversational agent to maintain the spatial attention of the child and to increase her motivation to undergo a long-term therapy

A multi-projector CAVE system with commodity hardware and gesture-based interaction

Spatially-immersive systems such as CAVEs provide users with surrounding worlds by projecting 3D models on multiple screens around the viewer. Compared to alternative immersive systems such as HMDs, CAVE systems are a powerful tool for collaborative inspection of virtual environments due to better use of peripheral vision, less sensitivity to tracking errors, and higher communication possibilities among users. Unfortunately, traditional CAVE setups require sophisticated equipment including stereo-ready projectors and tracking systems with high acquisition and maintenance costs. In this paper we present the design and construction of a passive-stereo, four-wall CAVE system based on commodity hardware. Our system works with any mix of a wide range of projector models that can be replaced independently at any time, and achieves high resolution and brightness at a minimum cost. The key ingredients of our CAVE are a self-calibration approach that guarantees continuity across the screen, as well as a gesture-based interaction approach based on a clever combination of skeletal data from multiple Kinect sensors.Preprin

Fast and Robust Hand Tracking Using Detection-Guided Optimization

Markerless tracking of hands and fingers is a promising enabler for human-computer interaction. However, adoption has been limited because of tracking inaccuracies, incomplete coverage of motions, low framerate, complex camera setups, and high computational requirements. In this paper, we present a fast method for accurately tracking rapid and complex articulations of the hand using a single depth camera. Our algorithm uses a novel detection-guided optimization strategy that increases the robustness and speed of pose estimation. In the detection step, a randomized decision forest classifies pixels into parts of the hand. In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth. Our approach needs comparably less computational resources which makes it extremely fast (50 fps without GPU support). The approach also supports varying static, or moving, camera-to-scene arrangements. We show the benefits of our method by evaluating on public datasets and comparing against previous work

Robot guidance using machine vision techniques in industrial environments: A comparative review

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works

A Survey of Augmented, Mixed and Virtual Reality for Cultural Heritage

A multimedia approach to the diffusion, communication, and exploitation of Cultural Heritage (CH) is a well-established trend worldwide. Several studies demonstrate that the use of new and combined media enhances how culture is experienced. The benefit is in terms of both number of people who can have access to knowledge and the quality of the diffusion of the knowledge itself. In this regard, CH uses augmented-, virtual-, and mixed-reality technologies for different purposes, including education, exhibition enhancement, exploration, reconstruction, and virtual museums. These technologies enable user-centred presentation and make cultural heritage digitally accessible, especially when physical access is constrained. A number of surveys of these emerging technologies have been conducted; however, they are either not domain specific or lack a holistic perspective in that they do not cover all the aspects of the technology. A review of these technologies from a cultural heritage perspective is therefore warranted. Accordingly, our article surveys the state-of-the-art in augmented-, virtual-, and mixed-reality systems as a whole and from a cultural heritage perspective. In addition, we identify specific application areas in digital cultural heritage and make suggestions as to which technology is most appropriate in each case. Finally, the article predicts future research directions for augmented and virtual reality, with a particular focus on interaction interfaces and explores the implications for the cultural heritage domain

Continuous Human Activity Tracking over a Large Area with Multiple Kinect Sensors

In recent years, researchers had been inquisitive about the use of technology to enhance the healthcare and wellness of patients with dementia. Dementia symptoms are associated with the decline in thinking skills and memory severe enough to reduce a person’s ability to pay attention and perform daily activities. Progression of dementia can be assessed by monitoring the daily activities of the patients. This thesis encompasses continuous localization and behavioral analysis of patient’s motion pattern over a wide area indoor living space using multiple calibrated Kinect sensors connected over the network. The skeleton data from all the sensor is transferred to the host computer via TCP sockets into Unity software where it is integrated into a single world coordinate system using calibration technique. Multiple cameras are placed with some overlap in the field of view for the successful calibration of the cameras and continuous tracking of the patients. Localization and behavioral data are stored in a CSV file for further analysis