arXiv subjects

Abdulmotaleb El Saddik

Publications and source records attributed to Abdulmotaleb El Saddik.

67 records · Page 4Linked to original sources

arXiv2022

Development of a Self-Calibrated Motion Capture System by Nonlinear Trilateration of Multiple Kinects v2

In this paper, a Kinect-based distributed and real-time motion capture system is developed. A trigonometric method is applied to calculate the relative position of Kinect v2 sensors with a calibration wand and register the sensors' positions automatically. By combining results from multiple sensors with a nonlinear least square method, the accuracy of the motion capture is optimized. Moreover, to exclude inaccurate results from sensors, a computational geometry is applied in the occlusion approach, which discovers occluded joint data. The synchronization approach is based on an NTP protocol that synchronizes the time between the clocks of a server and clients dynamically, ensuring that the proposed system is a real-time system. Experiments for validating the proposed system are conducted from the perspective of calibration, occlusion, accuracy, and efficiency. Furthermore, to demonstrate the practical performance of our system, a comparison of previously developed motion capture systems (the linear trilateration approach and the geometric trilateration approach) with the benchmark OptiTrack system is conducted, therein showing that the accuracy of our proposed system is $38.3\%$ and 24.1% better than the two aforementioned trilateration systems, respectively.

cs.RO↗

arXiv2022

Learning to Estimate 3D Human Pose from Point Cloud

3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2D depth images to 3D point clouds and directly predict the 3D joint position. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. The reported results on both ITOP and EVAL datasets demonstrate the effectiveness of our method on the targeted tasks.

cs.CV↗

arXiv2022

Sitting Posture Recognition Using a Spiking Neural Network

To increase the quality of citizens' lives, we designed a personalized smart chair system to recognize sitting behaviors. The system can receive surface pressure data from the designed sensor and provide feedback for guiding the user towards proper sitting postures. We used a liquid state machine and a logistic regression classifier to construct a spiking neural network for classifying 15 sitting postures. To allow this system to read our pressure data into the spiking neurons, we designed an algorithm to encode map-like data into cosine-rank sparsity data. The experimental results consisting of 15 sitting postures from 19 participants show that the prediction precision of our SNN is 88.52%.

eess.SP↗

arXiv2022

3-D Markerless Tracking of Human Gait by Geometric Trilateration of Multiple Kinects

In this paper, we develop an integrated markerless gait tracking system with three Kinect v2 sensors. A geometric principle-based trilateration method is proposed for optimizing the accuracy of the measured gait data. To tackle the data synchronization problem among the Kinect clients and the server, a synchronization mechanism based on NTP (Network Time Protocol) is designed for synchronizing the server and Kinect clients' clocks. Furthermore, a time schedule is designed for timing each Kinect client's data transmission. In the experiment, participants are asked to perform a 60 s walk while the proposed tracking system obtains the participant's gait data. Six joints (including left hip, right hip, left knee, right knee, left ankle and right ankle) of the participants are tracked where the obtained gait data are described as 6000 {movements} of joint positions (1000 {movements} for each joint). The results show that the trilateration tracking result by the three Kinect sensors has a much higher accuracy compared with the accuracy measured by a single Kinect sensor. Within a randomly sampled time period (67.726 s in the experiment), 98.37% of the frames generated by the gait tracking system have timing errors less than 1 ms, which is much better than the default NTP service embedded in the Windows 8.1 operating system. The accuracy of the proposed system is quantitatively evaluated and verified by a comparison with a commercial medical system (Delsys Trigno Smart Sensor System).

cs.MM↗

arXiv2022

A Framework of Reconfigurable Transducer Nodes for Smart Home Environments

This letter presents a transducer network framework that supports the amalgamation of multiple transducers into single wireless nodes. This approach is aimed at decreasing energy consumption by reducing the number of wireless transceivers involved in such networks. To make wireless nodes easily reconfigurable, a plug and play mechanism is applied to enable the clustering of any number of transducers. Furthermore, an algorithm is proposed to dynamically detect added and removed transducers from a node. Lastly, an XML based protocol is devised to allow nodes to communicate a description of their layout, measured data and control information. To verify the proposed framework, multiple reconfigurable wireless nodes are used to monitor the dynamic condition of a multiple rooms during a period of 24 hours in order to emulate a smart home scenario.

cs.NI↗

arXiv2022

Technical Evaluation of HoloLens for Multimedia: A First Look

A recently released cutting-edge AR device, Microsoft HoloLens, has attracted considerable attention with its advanced capabilities. In this article, we report the design and execution of a series of experiments to quantitatively evaluate HoloLens' performance in head localization, real environment reconstruction, spatial mapping, hologram visualization, and speech recognition.

cs.MM↗

arXiv2022

Towards a QoE Model to Evaluate Holographic Augmented Reality Devices

Augmented reality (AR) technology is developing fast and provides users with new ways to interact with the real-world surrounding environment. Although the performance of holographic AR multimedia devices can be measured with traditional quality-of-service parameters, a quality-of-experience (QoE) model can better evaluate the device from the perspective of users. As there are currently no well-recognized models for measuring the QoE of a holographic AR multimedia device, we present a QoE framework and model it with a fuzzy inference system to quantitatively evaluate the device.

cs.MM↗

arXiv2022

EVM-CNN: Real-Time Contactless Heart Rate Estimation from Facial Video

With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated the heart rate (HR) from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, like the processing time increasing with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that HR information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this paper to remotely estimate the HR under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach shows better performance compared with the benchmark on the MMSE-HR dataset in terms of both the average HR estimation and short-time HR estimation. High consistency in short-time HR estimation is observed between our method and the ground truth.

cs.CV↗

arXiv2022

A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion

We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.

cs.CV↗

arXiv2022

Interacting with New York City Data by HoloLens through Remote Rendering

In the digital era, Extended Reality (XR) is considered the next frontier. However, XR systems are computationally intensive, and they must be implemented within strict latency constraints. Thus, XR devices with finite computing resources are limited in terms of quality of experience (QoE) they can offer, particularly in cases of big 3D data. This problem can be effectively addressed by offloading the highly intensive rendering tasks to a remote server. Therefore, we proposed a remote rendering enabled XR system that presents the 3D city model of New York City on the Microsoft HoloLens. Experimental results indicate that remote rendering outperforms local rendering for the New York City model with significant improvement in average QoE by at least 21%. Additionally, we clarified the network traffic pattern in the proposed XR system developed under the OpenXR standard.

cs.MM↗

arXiv2022

Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaverse

The advances in Artificial Intelligence (AI) have led to technological advancements in a plethora of domains. Healthcare, education, and smart city services are now enriched with AI capabilities. These technological advancements would not have been realized without the assistance of fast, secure, and fault-tolerant communication media. Traditional processing, communication and storage technologies cannot maintain high levels of scalability and user experience for immersive services. The metaverse is an immersive three-dimensional (3D) virtual world that integrates fantasy and reality into a virtual environment using advanced virtual reality (VR) and augmented reality (AR) devices. Such an environment is still being developed and requires extensive research in order for it to be realized to its highest attainable levels. In this article, we discuss some of the key issues required in order to attain realization of metaverse services. We propose a framework that integrates digital twin (DT) with other advanced technologies such as the sixth generation (6G) communication network, blockchain, and AI, to maintain continuous end-to-end metaverse services. This article also outlines requirements for an integrated, DT-enabled metaverse framework and provides a look ahead into the evolving topic.

cs.HC↗

arXiv2022

Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation

The current studies of Scene Graph Generation (SGG) focus on solving the long-tailed problem for generating unbiased scene graphs. However, most de-biasing methods overemphasize the tail predicates and underestimate head ones throughout training, thereby wrecking the representation ability of head predicate features. Furthermore, these impaired features from head predicates harm the learning of tail predicates. In fact, the inference of tail predicates heavily depends on the general patterns learned from head ones, e.g., "standing on" depends on "on". Thus, these de-biasing SGG methods can neither achieve excellent performance on tail predicates nor satisfying behaviors on head ones. To address this issue, we propose a Dual-branch Hybrid Learning network (DHL) to take care of both head predicates and tail ones for SGG, including a Coarse-grained Learning Branch (CLB) and a Fine-grained Learning Branch (FLB). Specifically, the CLB is responsible for learning expertise and robust features of head predicates, while the FLB is expected to predict informative tail predicates. Furthermore, DHL is equipped with a Branch Curriculum Schedule (BCS) to make the two branches work well together. Experiments show that our approach achieves a new state-of-the-art performance on VG and GQA datasets and makes a trade-off between the performance of tail predicates and head ones. Moreover, extensive experiments on two downstream tasks (i.e., Image Captioning and Sentence-to-Graph Retrieval) further verify the generalization and practicability of our method.

cs.CV↗

arXiv2019

sZoom: A Framework for Automatic Zoom into High Resolution Surveillance Videos

Current cameras are capable of recording high resolution video. While viewing on a mobile device, a user can manually zoom into this high resolution video to get more detailed view of objects and activities. However, manual zooming is not suitable for surveillance and monitoring. It is tiring to continuously keep zooming into various regions of the video. Also, while viewing one region, the operator may miss activities in other regions. In this paper, we propose sZoom, a framework to automatically zoom into a high resolution surveillance video. The proposed framework selectively zooms into the sensitive regions of the video to present details of the scene, while still preserving the overall context required for situation assessment. A multi-variate Gaussian penalty is introduced to ensure full coverage of the scene. The method achieves near real-time performance through a number of timing optimizations. An extensive user study shows that, while watching a full HD video on a mobile device, the system enhances the security operator's efficiency in understanding the details of the scene by 99% on the average compared to a scaled version of the original high resolution video. The produced video achieved 46% higher ratings for usefulness in a surveillance task.

cs.MM↗