On the Query Strategies for Efficient Online Active Distillation

Deep Learning (DL) requires lots of time and data, resulting in high computational demands. Recently, researchers employ Active Learning (AL) and online distillation to enhance training efficiency and real-time model adaptation. This paper evaluates a set of query strategies to achieve the best training results. It focuses on Human Pose Estimation (HPE) applications, assessing the impact of selected frames during training using two approaches: a classical offline method and a online evaluation through a continual learning approach employing knowledge distillation, on a popular state-of-the-art HPE dataset. The paper demonstrates the possibility of enabling training at the edge lightweight models, adapting them effectively to new contexts in real-time

Orchestration-aware optimization of ROS2 communication protocols

The robot operating system (ROS) standard has been extended with different communication mechanisms to address real-time and scalability requirements. On the other hand, containerization and orchestration platforms like Docker and Kubernetes are increasingly being adopted to strengthen platform-independent development and automatic deployment of software. In this paper we quantitatively analyze the impact of topology, containerization, and edge-cloud distribution of ROS nodes on the efficiency of the ROS2 communication protocols. We then present a framework that automatically binds the most efficient ROS protocol for each node-to-node communication by considering architectural characteristics of both software and edge-cloud computing platform

Real-Time Multi-Person Identification and Tracking via HPE and IMU Data Fusion

In the context of smart environments, crafting re- mote monitoring systems that are efficient, cost-effective, user- friendly, and respectful of privacy is crucial for many scenar- ios. Recognizing and tracing individuals via markerless motion capture systems in multi-person settings poses challenges due to obstructions, varying light conditions, and intricate interactions among subjects. Nevertheless, methods based on data gathered by Inertial Measurement Units (IMUs) located in wearables grapple with other issues, including the precision of the sensors and their optimal placement on the body. We then argue that more accurate results can be achieved by mixing human pose estimation (HPE) techniques with information collected by wearables. Thus, this paper introduces a real-time platform to track and identify per- sons by fusing HPE and IMU data. It exploits a matching model that consists of two synergistic components: the first employs a geometric approach, correlating orientation, acceleration, and velocity readings from the input sources, while the second utilizes a Convolutional Neural Network (CNN) to yield a correlation coefficient for each HPE and IMU data pair. The proposed platform achieves promising results in tracking and identification, with an accuracy rate of 96.9%

Real-time Human Pose Estimation at the Edge for Gait Analysis at a Distance

Health telematics is a major improvement on patient lives and has shown to be a key practice to deliver healthcare services, overcoming geographical, temporal, and even organiza- tional barriers. One of the main challenges is to perform gait analysis at a distance through camera-based platforms, which requires the system to satisfy, beside accuracy and real-time, also portability and privacy compliance at the same time. We address this challenge by proposing a portable and low-cost platform that implements real-time and accurate 3D human pose estimation through an embedded software on a low-power off- the-shelf computing device that guarantees privacy by default and by design. We evaluated both accuracy and performance of the proposed solution through an infra-red marker-based motion capture system as ground truth to understand if and how such a portable technology can be used for gait analysis at a distance without leading to different clinical interpretations

On the nucleus structure and activity of comet 67P/Churyumov-Gerasimenko

Images from the OSIRIS scientific imaging system onboard Rosetta show that the nucleus of 67P/Churyumov-Gerasimenko consists of two lobes connected by a short neck. The nucleus has a bulk density less than half that of water. Activity at a distance from the Sun of >3 astronomical units is predominantly from the neck, where jets have been seen consistently. The nucleus rotates about the principal axis of momentum. The surface morphology suggests that the removal of larger volumes of material, possibly via explosive release of subsurface pressure or via creation of overhangs by sublimation, may be a major mass loss process. The shape raises the question of whether the two lobes represent a contact binary formed 4.5 billion years ago, or a single body where a gap has evolved via mass loss

Overview of the Rosetta Fly-by of 21-Lutetia as Observed by the OSIRIS Camera System

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