arXiv subjects

Anis Koubaa

Publications and source records attributed to Anis Koubaa.

52 records · Page 3Linked to original sources

arXiv2020

Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images

In this paper, we propose a deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images in a palm tree Farm in the Kharj region, in Riyadh Saudi Arabia, using DJI drones, and we built a dataset of around 10,000 instances of palms trees. Then, we developed a convolutional neural network model using the state-of-the-art, Faster R-CNN algorithm. Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to detect the geographical location of detected palms trees automatically. This geolocation technique was tested on two different types of drones (DJI Mavic Pro, and Phantom 4 Pro), and was assessed to provide an average geolocation accuracy of 2.8m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, it can be generalized to the geolocation of any other objects in UAV images.

cs.CV↗

arXiv2020

DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture

Aggressive driving (i.e., car drifting) is a dangerous behavior that puts human safety and life into a significant risk. This behavior is considered as an anomaly concerning the regular traffic in public transportation roads. Recent techniques in deep learning proposed new approaches for anomaly detection in different contexts such as pedestrian monitoring, street fighting, and threat detection. In this paper, we propose a new anomaly detection framework applied to the detection of aggressive driving behavior. Our contribution consists in the development of a 3D neural network architecture, based on the state-of-the-art EfficientNet 2D image classifier, for the aggressive driving detection in videos. We propose an EfficientNet3D CNN feature extractor for video analysis, and we compare it with existing feature extractors. We also created a dataset of car drifting in Saudi Arabian context https://www.youtube.com/watch?v=vLzgye1-d1k . To the best of our knowledge, this is the first work that addresses the problem of aggressive driving behavior using deep learning.

cs.CV↗

arXiv2020

Understanding the COVID19 Outbreak: A Comparative Data Analytics and Study

The Coronavirus, also known as the COVID-19 virus, has emerged in Wuhan China since late November 2019. Since that time, it has been spreading at large-scale until today all around the world. It is currently recognized as the world's most viral and severe epidemic spread in the last twenty years, as compared to Ebola 2014, MERS 2012, and SARS 2003. Despite being still in the middle of the outbreak, there is an urgent need to understand the impact of COVID-19. The objective is to clarify how it was spread so fast in a short time worldwide in unprecedented fashion. This paper represents a first initiative to achieve this goal, and it provides a comprehensive analytical study about the Coronavirus. The contribution of this paper consists in providing descriptive and predictive models that give insights into COVID-19 impact through the analysis of extensive data updated daily for the outbreak in all countries. We aim at answering several open questions: How does COVID-19 spread around the world? What is its impact in terms of confirmed and death cases at the continent, region, and country levels? How does its severity compare with other epidemic outbreaks, including Ebola 2014, MERS 2012, and SARS 2003? Is there a correlation between the number of confirmed cases and death cases? We present a comprehensive analytics visualization to address the questions mentioned above. To the best of our knowledge, this is the first systematic analytical papers that pave the way towards a better understanding of COVID-19. The analytical dashboards and collected data of this study are available online [1].

q-bio.PE↗

arXiv2020

AI-based Pilgrim Detection using Convolutional Neural Networks

Pilgrimage represents the most important Islamic religious gathering in the world where millions of pilgrims visit the holy places of Makkah and Madinah to perform their rituals. The safety and security of pilgrims is the highest priority for the authorities. In Makkah, 5000 cameras are spread around the holy for monitoring pilgrims, but it is almost impossible to track all events by humans considering the huge number of images collected every second. To address this issue, we propose to use artificial intelligence technique based on deep learning and convolution neural networks to detect and identify Pilgrims and their features. For this purpose, we built a comprehensive dataset for the detection of pilgrims and their genders. Then, we develop two convolutional neural networks based on YOLOv3 and Faster-RCNN for the detection of Pilgrims. Experiments results show that Faster RCNN with Inception v2 feature extractor provides the best mean average precision over all classes of 51%.

cs.CV↗

arXiv2019

Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

In the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures' perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assistive framework that guides worshippers to evaluate the correctness of the postures of their prayers. This paper represents the first step to achieve this objective and addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN). The contribution of this paper lies in building a dataset for the basic Salat positions, and train a YOLOv3 neural network for the recognition of the gestures. Experimental results demonstrate that the mean average precision attains 85% for a training dataset of 764 images of the different postures. To the best of our knowledge, this is the first work that addresses human activity recognition of Salat using deep learning.

cs.CV↗

arXiv2019

Smart Palm: An IoT Framework for Red Palm Weevil Early Detection

Smart agriculture is an evolving trend in agriculture industry, where sensors are embedded into plants to collect vital data and help in decision making to ensure higher quality of crops and prevent pests, disease, and other possible threats. In Saudi Arabia, growing palms is the most important agricultural activity, and there is an increasing need to leverage smart agriculture technology to improve the production of dates and prevent diseases. One of the most critical diseases of palms if the red palm weevil, which is an insect that causes a lot of damage to palm trees and can devast large areas of palm trees. The most challenging problem is that the effect of the weevil is not visible by humans until the palm reaches an advanced infestation state. For this reason, there is a need to use advanced technology for early detection and prevention of infestation propagation. In this project, we have developed am IoT based smart palm monitoring prototype as a proof-of-concept that (1) allows to monitor palms remotely using smart agriculture sensors, (2) contribute to the early detection of red palm weevil. Users can use web/mobile application to interact with their palm farms and help them in getting early detection of possible infestations. We used Elm company IoT platform to interface between the sensor layer and the user layer. In addition, we have collected data using accelerometer sensors and we applied signal processing and statistical techniques to analyze collected data and determine a fingerprint of the infestation.

eess.SP↗

arXiv2019

Service-Oriented Software Architecture for Cloud Robotics

In this article, we present an overview of the use of service-oriented architecture and Web services in developing robotics applications and software integrated with the Internet and the Cloud. This is a recent trend that emerged since 2010 from the concept of cloud robotics, which leverages the use of cloud infrastructures for robotics applications following a service-oriented architecture approach. In particular, we distinguish two main categories: (\textit{i.}) virtualization of robotics systems and (\textit{ii.}) computation offloading from robots to cloud-based services. We discuss the main approaches proposed in the literature to design robotics systems through the Web and their integration to the cloud through a service-oriented computing framework.

cs.RO↗

arXiv2019

Micro Air Vehicle Link (MAVLink) in a Nutshell: A Survey

The Micro Air Vehicle Link (MAVLink in short) is a communication protocol for unmanned systems (e.g., drones, robots). It specifies a comprehensive set of messages exchanged between unmanned systems and ground stations. This protocol is used in major autopilot systems, mainly ArduPilot and PX4, and provides powerful features not only for monitoring and controlling unmanned systems missions but also for their integration into the Internet. However, there is no technical survey and/or tutorial in the literature that presents these features or explains how to make use of them. Most of the references are online tutorials and basic technical reports, and none of them presents comprehensive and systematic coverage of the protocol. In this paper, we address this gap, and we propose an overview of the MAVLink protocol, the difference between its versions, and its potential in enabling Internet connectivity to unmanned systems. We also discuss the security aspects of MAVLink. To the best of our knowledge, this is the first technical survey and tutorial on the MAVLink protocol, which represents an important reference for unmanned systems users and developers.

cs.RO↗

arXiv2019

Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

Segmenting aerial images is being of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pre-trained segmentation model to survey a new city that is not included in the training set significantly decreases the accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We design an algorithm that reduces the domain shift impact using Generative Adversarial Networks (GANs). In the experiments, we test the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves the overall accuracy from 35% to 52% when passing from Potsdam domain (considered as source domain) to Vaihingen domain (considered as target domain). In addition, the method allows recovering efficiently the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%.

cs.CV↗

arXiv2019

MAVSec: Securing the MAVLink Protocol for Ardupilot/PX4 Unmanned Aerial Systems

The MAVLink is a lightweight communication protocol between Unmanned Aerial Vehicles (UAVs) and ground control stations (GCSs). It defines a set of bi-directional messages exchanged between a UAV (aka drone) and a ground station. The messages carry out information about the UAV's states and control commands sent from the ground station. However, the MAVLink protocol is not secure and has several vulnerabilities to different attacks that result in critical threats and safety concerns. Very few studies provided solutions to this problem. In this paper, we discuss the security vulnerabilities of the MAVLink protocol and propose MAVSec, a security-integrated mechanism for MAVLink that leverages the use of encryption algorithms to ensure the protection of exchanged MAVLink messages between UAVs and GCSs. To validate MAVSec, we implemented it in Ardupilot and evaluated the performance of different encryption algorithms (i.e. AES-CBC, AES-CTR, RC4, and ChaCha20) in terms of memory usage and CPU consumption. The experimental results show that ChaCha20 has a better performance and is more efficient than other encryption algorithms. Integrating ChaCha20 into MAVLink can guarantee its messages confidentiality, without affecting its performance, while occupying less memory and CPU consumption, thus, preserving memory and saving the battery for the resource-constrained drone.

cs.CR↗

arXiv2019

BlockLoc: Secure Localization in the Internet-of-Things using Blockchain

Several IoT applications are tightly dependent on the locations of the devices. However, localization algorithms can be easily compromised by injecting false locations. In this paper, we propose a Blockchain-based secure localization algorithm for the Internet of Things (IoT). The algorithm uses a public ledger (Blockchain) that contains nodes position and the list of their neighbor nodes. This ledger is shared among the IoT devices. Once an IoT device is localized its new position and the list of neighbor nodes are added to the Blockchain. This shared localization data will be used later by other IoT devices for their localization process. To avoid the attack where a malicious node sends a fake position, the correctness of the claimed position are verified before adding it to the Blockchain. Moreover, data exchanged between nodes (IoT devices) are signed to guarantee their authenticity and integrity. The integration of these security mechanisms into the localization process permits to exclude false data and therefore reduces the localization error. The simulation results show that adding the proposed security mechanism improves the localization accuracy of the algorithm when running in the presence of malicious nodes.

cs.CR↗

arXiv2019

Qualitative and Quantitative Risk Analysis and Safety Assessment of Unmanned Aerial Vehicles Missions over the Internet

In the last few years, Unmanned Aerial Vehicles (UAVs) are making a revolution as an emerging technology with many different applications in the military, civilian, and commercial fields. The advent of autonomous drones has initiated serious challenges, including how to maintain their safe operation during their missions. The safe operation of UAVs remains an open and sensitive issue since any unexpected behavior of the drone or any hazard would lead to potential risks that might be very severe. The motivation behind this work is to propose a methodology for the safety assurance of drones over the Internet {(Internet of drones (IoD))}. Two approaches will be used in performing the safety analysis: (1) a qualitative safety analysis approach, and (2) a quantitative safety analysis approach. The first approach uses the international safety standards, namely ISO 12100 and ISO 13849 to assess the safety of drone's missions by focusing on qualitative assessment techniques. The methodology starts with hazard identification, risk assessment, risk mitigation, and finally, draws the safety recommendations associated with a drone delivery use case. The second approach presents a method for the quantitative safety assessment using Bayesian Networks (BN) for probabilistic modeling. BN utilizes the information provided by the first approach to model the safety risks related to UAVs' flights. An illustrative UAV crash scenario is presented as a case study, followed by scenario analysis, to demonstrate the applicability of the proposed approach. These two analyses, qualitative and quantitative, enable { all involved stakeholders} to detect, explore and address the risks of UAV flights, which will help the industry to better manage the safety concerns of UAVs.

cs.RO↗

arXiv2019

Towards a Realistic Simulation Framework for Vehicular Platooning Applications

Cooperative vehicle platooning applications increasingly demand realistic simulation tools to ease their validation and to bridge the gap between development and real-world deployment. However, their complexity and cost often hinder its validation in the real world. In this paper, we propose a realistic simulation framework for vehicular platoons that integrates Gazebo with OMNeT++ over Robot Operating System (ROS) to support the simulation of realistic scenarios of autonomous vehicular platoons and their cooperative control.

cs.RO↗

arXiv2019

A Commodity SBC-Edge Cluster for Smart Cities

The commodity Single Board Computers (SBCs) are increasingly becoming powerful and can execute standard operating systems and mainstream workloads. In the context of cloud-based smart city applications, SBCs can be utilized as Edge computing devices reducing the network communication. In this paper, we investigate the design and implementation of a SBC based edge cluster (SBC-EC) framework for a smart parking application. Since SBCs are resource constrained devices, we devise a container-based framework for a lighter foot-print. Kubernetes was used as an orchestration tool to orchestrate various containers in the framework. To validate our approach, we implemented a proof-of-concept of the SBC based Edge cluster for a smart parking application, as a possible IoT use-case. Our implementation shows that, the use of SBC devices at the edge of a cloud based smart parking application is a cost effective and low energy, green computing solution. The proposed framework can be extended to similar cloud-based applications in the context of a smart city.

cs.CY↗

arXiv2019

On Energy Efficiency and Performance Evaluation of SBC based Clusters: A Hadoop case study

Energy efficiency in a data center is a challenge and has garnered researchers interest. In this paper we address the energy efficiency issue of a small scale data center by utilizing Single Board Computer (SBC) based clusters. A compact design layout is presented to build two clusters using 20 nodes each. Extensive testing was carried out to analyze the performance of these clusters using popular performance benchmarks for task execution time, memory/storage utilization, network throughput and energy consumption. Further, we investigate the cost of operating SBC based clusters by correlating energy utilization for the execution time of various benchmarks using workloads of different sizes. Results show that, although the low-cost benefit of a cluster built with ARM-based SBCs is desirable, these clusters yield low comparable performance and energy efficiency due to limited onboard capabilities. It is possible to tweak Hadoop configuration parameters for an ARM-based SBC cluster to efficiently utilize resources. We present, a discussion on the effectiveness of the SBC-based clusters as a testbed for inexpensive and green cloud computing research.

cs.DC↗

arXiv2018

Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

Unmanned Aerial Vehicles are increasingly being used in surveillance and traffic monitoring thanks to their high mobility and ability to cover areas at different altitudes and locations. One of the major challenges is to use aerial images to accurately detect cars and count them in real-time for traffic monitoring purposes. Several deep learning techniques were recently proposed based on convolution neural network (CNN) for real-time classification and recognition in computer vision. However, their performance depends on the scenarios where they are used. In this paper, we investigate the performance of two state-of-the-art CNN algorithms, namely Faster R-CNN and YOLOv3, in the context of car detection from aerial images. We trained and tested these two models on a large car dataset taken from UAVs. We demonstrated in this paper that YOLOv3 outperforms Faster R-CNN in sensitivity and processing time, although they are comparable in the precision metric.

cs.RO↗