The widespread adoption of depth cameras has been driven by their cost-effectiveness and depth measurement reliability across diverse applications. These sensors, however, face common challenges inherent to optical systems, particularly motion blur artifacts when deployed on moving platforms. This paper introduces a novel end-to-end approach for motion blur removal in Amplitude Modulated Continuous Wave (AMCW) depth cameras, specifically designed to handle High Dynamic Range (HDR) depth imagery. Our method leverages an optical flow network trained on Differential Correlation Sample (DCS) images to achieve proper alignment prior to phase unwrapping. We present a new real-HDR DCS dataset and develop an unsupervised training framework utilizing custom loss functions with adaptive scheduling, eliminating the need for ground truth data. While our implementation did not achieve fully successful optical flow generation for phase unwrapping, our experiments demonstrate the potential viability of optical flow-based approaches for depth image deblurring.
The emergency evacuation systems are often installed in crowded places such as airports, train stations, and shopping malls to evacuate and protect people when incidents occur quickly. With the development of surveillance cameras, vision-based emergency evacuation systems have demonstrated their ability to observe and promptly warn flexibly. This paper proposes a human behavior detector by fine-tuning the YOLOv11n detection network with the Global Attention Mechanism (GAM) to enhance the individual human action recognition. Extensive experiments are trained and evaluated on the Human Behavior Detection Dataset (HBDset) using a NVIDIA Tesla V100 32GB GPU. The proposed network achieves 62.2% of mAP and an inference speed of 1.3 milliseconds (ms), and outperforms other networks of the same scale.
The paper presents preliminary analyses of pulse transit time (PTT) derived from photoplethysmography (PPG) measurements taken at the elbow, wrist, and finger. Data were collected from ten participants under the approval of a bioethics committee. The study examined the correlation coefficients of PTT between these locations using four fiducial points of the PPG signal. The results show strong dependence on the individual participant, the choice of fiducial point, and the sensor placement (the correlation coefficient varies from -0.69 to 1.00). These findings highlight the need for further analysis on a larger and more diverse population, including individuals with varying health conditions and broader ranges of blood pressure fluctuations.
Currently, monocular 3D object detection has attracted increasing attention due to its cost-effectiveness and scalability compared to LiDAR-based solutions or stereo-based solutions. However, the inherent absence of depth information in a single RGB image introduces significant uncertainty in 3D localization and pose estimation of objects. To address the ambiguity in estimating object dimensions and depth, many existing approaches rely on complex and heavy neural network architectures, which often hinder their practicality in real-world applications. This paper proposes a novel and lightweight multi-task framework for monocular 3D object detection, which leverages keypoint estimation and 2D object detection to infer 3D spatial attributes. Extensive experiments conducted on the KITTI benchmark demonstrate the efficiency and effectiveness of the proposed method. Notably, our model achieves an inference speed of up to 40 FPS, making it highly suitable for real-time deployment in intelligent driving scenarios.
The advancement of robotics has been driven by the integration of artificial intelligence, machine learning, and sophisticated sensing technologies, enabling more seamless Human-Robot Interaction (HRI). Facial Attribute Classifier (FAC) plays a crucial role in HRI by helping robots understand human emotions, intentions, and social cues, fostering personalized and intuitive interactions. However, while existing methods achieve high accuracy, their computational complexity limits real-time applications on low-cost or CPU-based devices, highlighting the need for lightweight models that balance accuracy and efficiency. This work proposes an Efficient Network (ENet) designed to achieve an optimal trade-off between efficiency and accuracy of FAC. ENet introduces an Enhanced Sequential Efficient Attention Module (ESEAM) to improve the quality of feature maps while maintaining high efficiency. Accordingly, ENet demonstrates a compromise between efficiency and accuracy on the CelebA and LFWA datasets. The proposed ENet is computationally efficient, generating a few parameters, making it well-suited for CPU-based applications. When combined with a face detector, the optimized FAC achieves a processing speed of 25.88 frames per second (FPS) on an Intel Core i7-9750H CPU, demonstrating its suitability for real-time use.
