Search Results for author: Hamid Menouar

Found 13 papers, 1 papers with code

Haris: an Advanced Autonomous Mobile Robot for Smart Parking Assistance

no code implementations31 Jan 2024 Layth Hamad, Muhammad Asif Khan, Hamid Menouar, Fethi Filali, Amr Mohamed

This paper presents Haris, an advanced autonomous mobile robot system for tracking the location of vehicles in crowded car parks using license plate recognition.

Autonomous Navigation License Plate Recognition +4

Curriculum for Crowd Counting -- Is it Worthy?

no code implementations15 Jan 2024 Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

In this work, we investigate the impact of curriculum learning in crowd counting using the density estimation method.

Crowd Counting Density Estimation

Multimodal Crowd Counting with Pix2Pix GANs

no code implementations15 Jan 2024 Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Recently, some studies have reported improvement in the accuracy of crowd counting models using a combination of RGB and thermal images.

Crowd Counting

Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs

no code implementations11 Oct 2023 Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs).

Crowd Counting Generative Adversarial Network +1

Visual Crowd Analysis: Open Research Problems

no code implementations21 Aug 2023 Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome.

Visual Crowd Analysis

CLIP: Train Faster with Less Data

no code implementations2 Dec 2022 Muhammad Asif Khan, Ridha Hamila, Hamid Menouar

CLIP combines two data-centric approaches i. e., curriculum learning and dataset pruning to improve the model learning accuracy and convergence speed.

Density Estimation

Crowd Density Estimation using Imperfect Labels

no code implementations2 Dec 2022 Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Density estimation is one of the most widely used methods for crowd counting in which a deep learning model learns from head-annotated crowd images to estimate crowd density in unseen images.

Crowd Counting Density Estimation +1

Unauthorized Drone Detection: Experiments and Prototypes

no code implementations2 Dec 2022 Muhammad Asif Khan, Hamid Menouar, Osama Muhammad Khalid, Adnan Abu-Dayya

Owing to the limitations of these schemes, we present a novel encryption-based drone detection scheme that uses a two-stage verification of the drone's received signal strength indicator (RSSI) and the encryption key generated from the drone's position coordinates to reliably detect an unauthorized drone in the presence of authorized drones.

Position

DroneNet: Crowd Density Estimation using Self-ONNs for Drones

no code implementations14 Nov 2022 Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Video surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios.

Crowd Counting Density Estimation

Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives

no code implementations14 Sep 2022 Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

In this paper, we present a systematic and comprehensive review of the most significant contributions in the area of crowd counting.

Crowd Counting

Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

1 code implementation23 Apr 2022 Wei Shao, Zhiling Jin, Shuo Wang, Yufan Kang, Xiao Xiao, Hamid Menouar, Zhaofeng Zhang, Junshan Zhang, Flora Salim

To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.

Graph Attention Spatio-Temporal Forecasting

An Improved Dilated Convolutional Network for Herd Counting in Crowded Scenes

no code implementations17 Aug 2020 Soufien Hamrouni, Hakim Ghazzai, Hamid Menouar, Yahya Massoud

In this paper, we propose an accurate monitoring system composed of two concatenated convolutional deep learning architectures.

Management

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