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6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Gazzeh, Soulayma Douik, Ali ENISo University of Sousse Laboratory of Networked Objects Control and Communications Systems Sousse Tunisia

ISBN: (纸本)9781665451161

Visual perception and road scene understanding are critical components of autonomous driving (AD). Detecting objects and predicting surrounding road user behavior are important tasks for robust and safe driving systems. With the rise of Machine Learning (ML) and Deep Learning (DL) methods using convolutional neural networks (CNN) , research on road scene understanding and motion recognition have made significant progress. In this paper, a real-time recognition of pedestrian's intention to cross is presented as a step towards autonomous driving. Our approach is constructed using a real-Time visual pedestrian tracker based on a one-stage object detector that gave a fast and robust learning produces dynamic and static features as inputs of a linear classifier for predicting crossing behavior. We apply our approach to public real-life benchmark - JAAD dataset "Joint Attention in Autonomous Driving."The experimental results have shown that our proposed method is efficient and competitive with other approaches. Overall, our system achieves an accuracy of 92.88%. © 2022 IEEE.

关键词： Autonomous vehicles

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Deep learning traffic sign detection, recognition and augmentation 17

Deep learning traffic sign detection, recognition and augmen...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Abdi, Lotfi Meddeb, Aref National Engineering School of Tunis University of Tunis Networked Objects Control and Communication Systems Laboratory El Manar Tunisia National Engineering School of Sousse University of Sousse Networked Objects Control and Communication Systems Laboratory Tunisia

ISBN: (纸本)9781450344869

Driving is a complex, continuous, and multitask process that involves driver's cognition, perception, and motor movements. The way road traffic signs and vehicle information is displayed impacts strongly driver's attention with increased mental workload leading to safety concerns. Drivers must keep their eyes on the road, but can always use some assistance in maintaining their awareness and directing their attention to potential emerging hazards. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. In-vehicle contextual Augmented Reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we present a new real-time approach for fast and accurate framework for traffic sign recognition, based on Cascade Deep learning and AR, which superimposes augmented virtual objects onto a real scene under all types of driving situations, including unfavorable weather conditions. Experiments results show that, by combining the Haar Cascade and deep convolutional neural networks show that the joint learning greatly enhances the capability of detection and still retains its realtime performance.

关键词： Augmented reality

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Semantic recognition: Unified framework for joint object detection and semantic segmentation 17

Semantic recognition: Unified framework for joint object det...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Abdi, Lotfi Meddeb, Aref National Engineering School of Tunis University of Tunis El Manar Networked Objects Control and Communication Systems Laboratory Tunisia National Engineering School of Sousse University of Sousse Networked Objects Control and Communication Systems Laboratory Tunisia

ISBN: (纸本)9781450344869

Understanding contents of an image, or scene labeling, is an important yet very challenging problem in artificial intelligence and computer vision to improve road safety. Semantic labeling and object detection in road scenes are strongly correlated tasks. Motivated by the complementary effect of the two tasks, we presented a novel framework to address the scene understanding problem. In this paper we propose a new framework for semantic labeling and object detection problem which is able to combine ideas from deep Convolutional Neural Network (CNN) for object detection and fully-connected Conditional Random Field (CRF) for segmenting and labeling. Specifically, we develop a new framework uses global image features to predict detection which drastically reduces its errors from background detections and a pairwise CRF is used as a post-processing step to enforce spatial consistency in the structured prediction. By combining the consistency between final detection results of CNN and CRF based graphical models, our unified framework can effectively leverage the advantages of leading techniques i.e., CRF and CNN for these two tasks. Exten- sive experiments on the PASCAL VOC 2007/2012 data sets demonstrate the effectiveness of our framework for Scene Understanding tasks. Copyright 2017 ACM.

关键词： Semantic Segmentation

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Driver information system: A combination of augmented reality and deep learning 17

Driver information system: A combination of augmented realit...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

ISBN: (纸本)9781450344869

Improving traffic safety is one of the important goals of Intelligent Transportation systems (ITS). In vehicle-based safety systems, it is more desirable to prevent an accident than to reduce severity of injuries. One of the solutions to prevent accidents is to provide information on the surrounding environment of the driver. Augmented Reality Head-Up Display (AR-HUD) can facilitate a new form of dialogue between the vehicle and the driver;and enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we propose a fast deeplearning-based object detection approaches for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. A single Convolutional Neural Network (CNN) predicts region of interest and class probabilities directly from full images in one evaluation. We also investigated potential costs and benefits of using dynamic conformal AR cues in improving driving safety. Copyright 2017 ACM.

关键词： Augmented reality

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Analysis and optimisation of 8 × 1 double 'U' slotted patch array for future 5G communications

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International Journal of systems, control and communications 2020年第3期11卷 305-319页

作者： Zaidi, Abir Baghdad, Abdennaceur Awan, Wahaj Abbas Faleh, Souhir Ballouk, Abdelhakim Badri, Abdelmajid Mohammedia Laboratory EEA and TI Hassan II University Casablanca Morocco Department of Electrical and Computer Engineering COMSTAS University Islamabad Sahiwal Pakistan Networked Objects Control and Communication Systems Laboratory National Engineers School of Sousse University of Sousse Sousse Tunisia

In this manuscript, an 8 × 1 rectangular 'U' slotted patch antenna array designed for future 5G communications is presented. The proposed antenna is designed using the 3D EM CSTv18 microwave studio based on the finite difference time domain (FDTD) method, and compared using HFSSv19 EM (high frequency structure simulator) based on the finite element (FEM) method. Three scenarios are considered in this work, the first one is the analysis of antenna performance of single element, regular antenna array and slotted antenna array in order to evince the performance of the proposed antenna for future 5G communications. The designed array antenna presents good results in term of return loss, efficiency, VSWR, input impedance and gain. Moreover, a comparison between the proposed work and other related works is presented. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Millimeter waves

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Face Expression Neutralisation Using Improved Variational Autoencoder 6

Face Expression Neutralisation Using Improved Variational Au...

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6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Wiem, Grina Ali, Douik University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse National Engineering School of Monastir Rue Ibn Jazzar Monastir5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Pôle Technologique de Sousse Route de Ceinture Sahloul Sousse4054 Tunisia

ISBN: (纸本)9781665451161

There is a high demand for realistic facial expressions in modern computer graphics and multimedia research. Unfortunately, synthesizing face expressions takes time, effort, and hard labour since high naturalism of face expressions is required. Despite recent advances in synthesizing realistic facial images, current generative models conflict to catch more involved image types, potentially due to their latent space simplified architecture. Furthermore, the union of a Generative Adversarial Network (GAN) with a Variational Autoencoder (VAE) by using usually the Adam optimizer has recently been studied and investigated in cutting-edge research. Hence, at tiem of learning the generator takes a long time and then overfit for a particular time instance. So, in this work, we will suggest integrating the VAE to GAN's architecture by using the Ranger optimizer into an unsupervised generative model that at once learns to encode, generate and compare dataset samples to bring out various real neutral faces in a reduced and optimal time. Next, we proposed the probability distributions called Kullback-Leibler Divergence (KLD) as an objective weighting scheme that helps us to measure just how much information we lose when we choose an approximation during training. Also, the Mean Square Error (MSE), the most commonly used loss function for regression is used as a metric of evaluation for the experiment tests. © 2022 IEEE.

关键词： Mean square error

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Video saliency using supervoxels 10th

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10th KES International Conference on Intelligent Interactive Multimedia systems and Services, IIMSS 2017

作者： Kalboussi, Rahma Abdellaoui, Mehrez Douik, Ali Networked Objects Control and Communication Systems Laboratory National Engineering School of Sousse Pôle technologique de Sousse Route de Ceinture Sahloul Sousse4054 Tunisia

ISBN: (纸本)9783319594798

Physiology and neural systems researchers revealed that the visual system is attracted by some parts of an image more than others. Different computational models were developed to simulate the visual system. In this paper we propose a video saliency model that helps to predict and detect the regions of interest in each video frame. We use a supervoxel segmentation as an indicator of dynamic objects. Based on the observation that dynamic objects attract attention when an observer watches a video sequence, supervoxel segmentation provides a first estimation for what belongs to foreground and background. Then, a saliency score is attributed to each supervoxel according to its motion distinctiveness. Experiments over two benchmark datasets, using several evaluation metrics have shown that our proposed method outperforms five saliency detection methods. © Springer International Publishing AG 2018.

关键词： Computers

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Parallel video processing on FPGA architecture

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International Journal of High Performance systems Architecture 2018年第3期8卷 169-178页

作者： Touil, Lamjed Abdelali, Abdessalem Ben Kechiche, Lilia Chaieb, Chiheb Ouni, Bouraoui Mtibaa, Abdellatif Networked Objects Control and Communications Systems Laboratory ENISo Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Tunisia Laboratory of Electronics and Microelectronics University of Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Tunisia Networked Objects Control and Communications Systems Laboratory ENISo University of Sousse Tunisia

Real-time video applications are becoming widely used in many domains with more demand for high performance. Video processing is intensive and habitually has accompanying real-time or super-real-time requirements. Such us, multiple cameras are used in monitoring and surveillance systems in automatically real-time analyse video to detect unusual events. Due to the strong computational imposed by video algorithms, real-time video treatment is notably amenable to concurrent processing. Classical implementation solutions whether based on general purpose processors or dedicated ones like DSP cannot fulfil wanted performance. In this article, we focus on the applicability of computing reconfigurable architectures to parallel video processing applications. The experiment results show that the proposed hardware-oriented multi-treatment architecture can provide an average frame rate of 45 frames/s at high definition resolution. Statistics show a consumption about 18% of logic resources and 27% of on chip memory which gives the possibility to integrate additional treatments. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Reconfigurable architectures

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Automatic Face Expression Neutralisation and Posture Correction via 3D Contrastive Learning 27

Automatic Face Expression Neutralisation and Posture Correct...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Wiem, Grina Ali, Douik University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center University of Monastir National Engineering School of Monastir Sahloul Belt Road 4054 Rue Ibn Jazzar Monastir5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 Tunisia

The performance of recognition systems can be significantly affected by various factors, with facial expression poses and lighting changes being the main confounding factors. In order to minimize their impact, we propose an intriguing method in this study that enables the generation of high-quality images specifically tailored to the target domain. Our objective is to utilize disentangled representation to effectively model the decomposition of data variations and generate neutral facial expression images with frontal posture and adaptive illumination. To achieve this, we incorporate 3D priors in the adversarial learning during the training process, simulating the generation of an analytical 3D face deformation as well as rendering operations. Additionally, we employ contrastive learning to control the disentanglement of the generated faces while preserving the essential properties of facial features. This technique enables us to learn an embedding space in which similar data samples are represented closely, while distinct samples are kept far apart from each other. Furthermore, we conduct an analysis of the learned latent space and introduce several other significant properties that enhance the reinforcement of factor disentanglement. These properties include an imitation learning algorithm, which facilitates the acquisition of meaningful patterns and characteristics. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： 3D contrastive learning disentanglement expression neutralization generative adversarial network

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control of the chaotic phenomenon in robot path using differential flatness 15

Control of the chaotic phenomenon in robot path using differ...

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15th International Conference on Informatics in control, Automation and Robotics, ICINCO 2018

作者： Nasr, Salah Abadi, Amine Bouallegue, Kais Mekki, Hassen Laboratory of Networked Objects Control and Communication Systems National Engineering School of Sousse University of Sousse Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse Tunisia

ISBN: (纸本)9789897583216

This paper deals with the complex chaotic behavior that can appear in the dynamic trajectory of a mobile robot, when the robot is broken or partly damaged. However, a flatness-based controller is designed to ensure the trajectory planning and tracking. Different mathematical tools have been used such as the flatness control technique and non linear chaotic systems. The simulation results for the kinematic controller are presented to demonstrate the effectiveness of this approach. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Mobile robots

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