Combining inertial and visual sensing for human action recognition in tennis

In this paper, we present a framework for both the automatic extraction of the temporal location of tennis strokes within a match and the subsequent classification of these as being either a serve, forehand or backhand. We employ the use of low-cost visual sensing and low-cost inertial sensing to achieve these aims, whereby a single modality can be used or a fusion of both classification strategies can be adopted if both modalities are available within a given capture scenario. This flexibility allows the framework to be applicable to a variety of user scenarios and hardware infrastructures. Our proposed approach is quantitatively evaluated using data captured from elite tennis players. Results point to the extremely accurate performance of the proposed approach irrespective of input modality configuration

Low computation vision-based navigation for a Martian rover

Construction and design details of the Mobot Vision System, a small, self-contained, mobile vision system, are presented. This system uses the view from the top of a small, roving, robotic vehicle to supply data that is processed in real-time to safely navigate the surface of Mars. A simple, low-computation algorithm for constructing a 3-D navigational map of the Martian environment to be used by the rover is discussed

Real-Time, Multiple Pan/Tilt/Zoom Computer Vision Tracking and 3D Positioning System for Unmanned Aerial System Metrology

The study of structural characteristics of Unmanned Aerial Systems (UASs) continues to be an important field of research for developing state of the art nano/micro systems. Development of a metrology system using computer vision (CV) tracking and 3D point extraction would provide an avenue for making these theoretical developments. This work provides a portable, scalable system capable of real-time tracking, zooming, and 3D position estimation of a UAS using multiple cameras. Current state-of-the-art photogrammetry systems use retro-reflective markers or single point lasers to obtain object poses and/or positions over time. Using a CV pan/tilt/zoom (PTZ) system has the potential to circumvent their limitations. The system developed in this paper exploits parallel-processing and the GPU for CV-tracking, using optical flow and known camera motion, in order to capture a moving object using two PTU cameras. The parallel-processing technique developed in this work is versatile, allowing the ability to test other CV methods with a PTZ system using known camera motion. Utilizing known camera poses, the object\u27s 3D position is estimated and focal lengths are estimated for filling the image to a desired amount. This system is tested against truth data obtained using an industrial system

Development of an Optronic Aiming System for Target Tracking on the S60 57mm Cannon Weapon Control System Using a Camera

The development of technological science towards the defense of a country is growing rapidly. The country needs increasingly advanced defense technology but is constrained by an increasingly large budget due to dependence on producing countries. The state provides opportunities for technocrats to carry out research that can later create a defense technology that is inexpensive and does not burden the state budget. The artificial S60 57mm cannon is one of the cannons that functions as an air attack deterrent. The operating system of this weapon is still manual. This research will design and prototype an automatic Weapon Control System on the S60 57mm gun. In this development using the Atmega 8 microcontroller is the controller and interface for transferring data from the camera (Optronic) to the servo motor. The result of the camera position (optronics) will be followed in real-time by the gun barrel. In designing a target tracking control system automatically using the proportional control method. The results of testing the ability of the servo motor as a camera driver to follow targets or moving objects with a maximum angular speed of 15.5 degrees/second at a speed of 0.3 seconds, the average frame rate of the camera are 60 fps. c. Based on testing the servo motor using the Atmega8 microcontroller support, the reading of the angle direction of the servo motor is displayed in the Delphi software. Based on the test results, there is a difference between mathematical calculations and measurements using an arc ruler. With mathematical calculations, there is a difference with a value of 1.06 degrees per 1 degree

高速ビジョンを用いたリアルタイムビデオモザイキングと安定化に関する研究

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Towards a Smart Drone Cinematographer for Filming Human Motion

Affordable consumer drones have made capturing aerial footage more convenient and accessible. However, shooting cinematic motion videos using a drone is challenging because it requires users to analyze dynamic scenarios while operating the controller. In this thesis, our task is to develop an autonomous drone cinematography system to capture cinematic videos of human motion. We understand the system's filming performance to be influenced by three key components: 1) video quality metric, which measures the aesthetic quality -- the angle, the distance, the image composition -- of the captured video, 2) visual feature, which encapsulates the visual elements that influence the filming style, and 3) camera planning, which is a decision-making model that predicts the next best movement. By analyzing these three components, we designed two autonomous drone cinematography systems using both heuristic-based methods and learning-based methods.For the first system, we designed an Autonomous CinemaTography system -- "ACT" by proposing a viewpoint quality metric focusing on the visibility of the 3D human skeleton of the subject. We expanded the application of human motion analysis and simplified manual control by assisting viewpoint selection using a through-the-lens method. For the second system, we designed an imitation-based system that learns the artistic intention of the cameramen through watching professional aerial videos. We designed a camera planner that analyzes the video contents and previous camera motion to predict future camera motion. Furthermore, we propose a planning framework, which can imitate a filming style by ``seeing" only one single demonstration video of such style. We named it ``one-shot imitation filming." To the best of our knowledge, this is the first work that extends imitation learning to autonomous filming. Experimental results in both simulation and field test exhibit significant improvements over existing techniques and our approach managed to help inexperienced pilots capture cinematic videos