As autonomous driving technology advances at an unprecedented pace, drivers are experiencing greater freedom within their vehicles, which accelerates the development of various intelligent systems to support safe and more efficient driving. These intelligent systems provide interactive applications between the vehicle and the driver, utilizing driver behavior analysis (DBA). A key performance indicator is real-time driver monitoring quality, as it directly impacts both safety and convenience in vehicle operation. In order to achieve real-time interaction, an image processing speed exceeding 30 frames/s and a delay time (latency) below 100 ms are generally required. However, expensive devices are often necessary to support this with software. Therefore, this article presents an algorithm and implementation results for immediate in-vehicle DBA through field-programmable gate array (FPGA)-based high-speed upper body-pose estimation. First, we define the 11 key points related to the driver’s pose and gaze and model a convolutional neural network (CNN) architecture that can quickly detect them. The proposed algorithm utilizes regeneration and retraining through layer reduction based on the residual-CNN model. In addition, the algorithm presents the results of its implementation at the register transfer level (RTL) level of the VCU118 FPGA and demonstrates simulation results of 34.7 frames/s and a delay time of 75.3 ms. Lastly, we discuss the results of linking a demo application and creating a vehicle testbed to experiment with the driver–vehicle interaction (DVI) system. A developed FPGA platform is implemented to process camera image input in real time. It reliably supports detected pose and gaze results at 30 frames/s via Ethernet. It also presents results that verify its application in screen control and driver monitoring systems.

In this paper, a design and implementation of fixed-complexity sphere decoder (FSD) combined with interference mitigation algorithm for downlink (DL) multiuser (MU) multiple-input multiple-output (MIMO) system is presented. To overcome performance degradation from inter-user interference in DL MU-MIMO systems, a hardware friendly optimized interference mitigation algorithm is proposed. The proposed algorithm achieves 0.5 dB near-optimal performance, which is 4 dB better than existing interference whitening filter for the soft-output decision. In this paper, both hard and soft-output FSD detectors are implemented with the proposed interference mitigation algorithm to function $4\times 4$ antenna MIMO detection and interference mitigation for 4 additional streams. A fully pipelined architecture is employed to support 3.6 Gbps at 150 MHz for signals modulated by 64-QAM. From the synthesis result, the designed symbol detector has a gate count of 887 K for the FSD without pre-processor and a gate count of 1220 K for interference mitigation. By comparing previously implemented MIMO detectors, the proposed design represents a promising architecture for DL MU-MIMO systems in terms of significant performance improvement relative to inter-user interference and the compatible hardware implementation maintaining high-throughput within acceptable gate count.

The preservation and digital transmission of cultural heritage have become increasingly vital in the era of immersive media. This study introduces a real-time framework for digitizing and animating traditional performing arts, with a focus on Korean traditional dance as a representative case study. The proposed approach combines three core components: (1) high-fidelity 3D avatar creation through volumetric scanning of performers, costumes, and props; (2) real-time motion capture using multi-camera edge processing; and (3) motion-to-avatar animation that integrates skeletal mapping with physics-based simulation. By transmitting only essential motion keypoints from lightweight edge devices to a central server, the system enables bandwidth-efficient streaming while reconstructing expressive, lifelike 3D avatars. Experiments with eight performers and eight cameras achieved low latency (~200 ms) and minimal network load (<1 Mbps), successfully reproducing the esthetic qualities and embodied gestures of Korean traditional performances in a virtual environment. Beyond its technical contributions, this framework provides a novel pathway for the preservation, dissemination, and immersive re-experiencing of intangible cultural heritage, ensuring that the artistry of traditional dance can be sustained and appreciated in digital form.

In this paper, we present a noise-robust approach for the 3D pose estimation of multiple people using appearance similarity. The common methods identify the cross-view correspondences between the detected keypoints and determine their association with a specific person by measuring the distances between the epipolar lines and the joint locations of the 2D keypoints across all the views. Although existing methods achieve remarkable accuracy, they are still sensitive to camera calibration, making them unsuitable for noisy environments where any of the cameras slightly change angle or position. To address these limitations and fix camera calibration error in real-time, we propose a framework for 3D pose estimation which uses appearance similarity. In the proposed framework, we detect the 2D keypoints and extract the appearance feature and transfer it to the central server. The central server uses geometrical affinity and appearance similarity to match the detected 2D human poses to each person. Then, it compares these two groups to identify calibration errors. If a camera with the wrong calibration is identified, the central server fixes the calibration error, ensuring accuracy in the 3D reconstruction of skeletons. In the experimental environment, we verified that the proposed algorithm is robust against false geometrical errors. It achieves around 11.5% and 8% improvement in the accuracy of 3D pose estimation on the Campus and Shelf datasets, respectively.