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IDFusion: Joint Angle Measurement Method through Fusion of Inertial Measurement Sensor and Depth Camera
Juyeon Park, Mingyu Park, Gyumin Park, Hyun Lee
http://doi.org/10.5626/JOK.2025.52.3.208
Recent advancements in human and object recognition technologies are increasingly applied across various fields, particularly in motion detection research utilizing inertial measurement sensors and depth cameras in areas such as gaming, healthcare, and security. However, challenges such as cumulative errors and variable measurement accuracies depending on the environment persist. This study proposed IDFusion, a method that could integrate inertial measurement sensors and depth cameras for joint angle measurement, distinguishing itself through data transformation and joint angle conversion stages before fusion. Comparative analysis against using inertial measurement sensors and depth cameras individually demonstrated a superior performance of IDFusion. This technique holds promise for applications in healthcare, sports science, and human-computer interaction.
Projection Model and Calibration Method for Multi-camera Systems with Ultra-wide Field-of-view Fisheye Lenses
http://doi.org/10.5626/JOK.2024.51.11.1005
Fisheye lenses with a wide field-of-view (FoV) are commonly utilized as sensors for mobile platforms such as cars and robots due to their capacity to recognize a large area at once. Algorithms that utilize multiple fisheye cameras to estimate distances and platform movements in all directions of 360 degrees have been developed and are presently deployed as robot sensors and for 3D modeling. In this paper, we present a novel projection model for fisheye lenses with an ultra-wide FoV exceeding 220 degrees. Next, we introduce a multi-camera system calibration method that can efficiently and accurately estimate the calibration of intrinsic and extrinsic parameters for a multi-camera system equipped with these lenses. The proposed methodology has successfully calibrated a range of systems, spanning from compact helmet-mounted capture devices to larger car-mounted systems. The experimental results show that the proposed method achieves sub-pixel calibration accuracy.
Height and Texture Modeling of Road Surfaces for Camera-Based HDMap Construction
http://doi.org/10.5626/JOK.2024.51.9.835
With increasing demand for construction and updating of HD maps for autonomous vehicles, multi-camera systems are being used as cost-effective sensors for Mobile Mapping System (MMS). Stereo matching among multi-view images, image feature point matching, and visual localization are utilized for such camera-based 3D map reconstruction. In this paper, we proposed a methodology for estimating height of road surfaces and texture registration utilizing hexgrid model, keyframe poses and 3D point clouds based on multi-view images. The proposed methodology could reconstruct high-density and high-accuracy 3D point cloud of road surfaces base on a multi-camera system mounted on the upper part of a vehicle. Our experimental results showed that the proposed method created a precise road model with a minimum point spacing of 0.025 m on a MMS equipped with ultra-wide angle fisheye cameras and GPS.
A Best View Selection Method in Videos of Interested Player Captured by Multiple Cameras
Hotak Hong, Gimun Um, Jongho Nang
http://doi.org/10.5626/JOK.2017.44.12.1319
In recent years, the number of video cameras that are used to record and broadcast live sporting events has increased, and selecting the shots with the best view from multiple cameras has been an actively researched topic. Existing approaches have assumed that the background in video is fixed. However, this paper proposes a best view selection method for cases in which the background is not fixed. In our study, an athlete of interest was recorded in video during motion with multiple cameras. Then, each frame from all cameras is analyzed for establishing rules to select the best view. The frames were selected using our system and are compared with what human viewers have indicated as being the most desirable. For the evaluation, we asked each of 20 non-specialists to pick the best and worst views. The set of the best views that were selected the most coincided with 54.5% of the frame selection using our proposed method. On the other hand, the set of views most selected as worst through human selection coincided with 9% of best view shots selected using our method, demonstrating the efficacy of our proposed method.
Real-Time Panorama Video Generation System using Multiple Networked Cameras
Panoramic image creation has been extensively studied. Existing methods use customized hardware, or apply post-processing methods to seamlessly stitch images. These result in an increase in either cost or complexity. In addition, images can only be stitched under certain conditions such as existence of characteristic points of the images. This paper proposes a low cost and easy-to-use system that produces realtime panoramic video. We use an off-the-shelf embedded platform to capture multiple images, and these are then transmitted to a server in a compressed format to be merged into a single panoramic video. Finally, we analyze the performance of the implemented system by measuring time to successfully create the panoramic image.
Taking a Jump Motion Picture Automatically by using Accelerometer of Smart Phones
This paper proposes algorithms to detect jump motion and automatically take a picture when the jump reaches its top. Based on the algorithms, we build jump-shot system by using accelerometer-equipped smart phones. Since the jump motion may vary depending on one"s physical condition, gender, and age, it is critical to figure out common features which are independent from such differences. Also it is obvious that the detection algorithm needs to work in real-time because of the short duration of the jump. We propose two different algorithms considering these requirements and develop the system as a smart phone application. Through a series of experiments, we show that the system is able to successfully detect the jump motion and take a picture when it reaches the top.
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