Search Results for author: Farzeen Munir

Found 11 papers, 3 papers with code

Radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks

no code implementations30 Oct 2023 Farzeen Munir, Shoaib Azam, Tomasz Kucner, Ville Kyrki, Moongu Jeon

This underscores the value of radar-Lidar fusion in achieving precise object detection and localization, especially in challenging weather conditions.

Object object-detection +3

Multi-Modal Fusion for Sensorimotor Coordination in Steering Angle Prediction

1 code implementation11 Feb 2022 Farzeen Munir, Shoaib Azam, Byung-Geun Lee, Moongu Jeon

The conventional frame-based RGB camera is the most common exteroceptive sensor modality used to acquire the environmental perception data.

Imitation Learning

ARTSeg: Employing Attention for Thermal images Semantic Segmentation

no code implementations30 Nov 2021 Farzeen Munir, Shoaib Azam, Unse Fatima, Moongu Jeon

Therefore, the neural network algorithms developed using these exteroceptive sensors have provided the necessary solution for the autonomous vehicle's perception.

Semantic Segmentation

SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving

no code implementations4 Mar 2021 Farzeen Munir, Shoaib Azam, Moongu Jeon

For this purpose, we have proposed a deep neural network Self Supervised Thermal Network (SSTN) for learning the feature embedding to maximize the information between visible and infrared spectrum domain by contrastive learning, and later employing these learned feature representation for the thermal object detection using multi-scale encoder-decoder transformer network.

Autonomous Driving Contrastive Learning +3

Channel Boosting Feature Ensemble for Radar-based Object Detection

no code implementations10 Jan 2021 Shoaib Azam, Farzeen Munir, Moongu Jeon

The proposed method's efficacy is extensively evaluated using the COCO evaluation metric, and the best-proposed model surpasses its state-of-the-art counterpart method by $12. 55\%$ and $12. 48\%$ in both good and good-bad weather conditions.

Autonomous Vehicles Object +2

LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor

1 code implementation17 Sep 2020 Farzeen Munir, Shoaib Azam, Moongu Jeon, Byung-Geun Lee, Witold Pedrycz

Traditional lane detection methods incorporate handcrafted or deep learning-based features followed by postprocessing techniques for lane extraction using frame-based RGB cameras.

Autonomous Driving Lane Detection

Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving

no code implementations1 Jun 2020 Farzeen Munir, Shoaib Azam, Muhammd Aasim Rafique, Ahmad Muqeem Sheri, Moongu Jeon, Witold Pedrycz

A thermal camera captures an image using the heat difference emitted by objects in the infrared spectrum, and object detection in thermal images becomes effective for autonomous driving in challenging conditions.

Autonomous Driving Domain Adaptation +6

N 2 C : Neural Network Controller Design Using Behavioral Cloning

no code implementations1 Jun 2020 Shoaib Azam, Farzeen Munir, Muhammad Aasim Rafique, Ahmad Muqeem Sheri, Muhammad Ishfaq Hussain, Moongu Jeon

In the first part of this study, we explore the pipeline of parsing decision commands from the path tracking algorithm to the controller and proposed a neural network-based controller ($N^2C$) using behavioral cloning.

Model Predictive Control Motion Planning

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

10 code implementations16 Feb 2020 Yeongmin Ko, Younkwan Lee, Shoaib Azam, Farzeen Munir, Moongu Jeon, Witold Pedrycz

In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system.

Autonomous Driving Clustering +4

pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications

no code implementations14 Nov 2017 Amina Asif, Wajid Arshad Abbasi, Farzeen Munir, Asa Ben-Hur, Fayyaz ul Amir Afsar Minhas

Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences.

General Classification Multiple Instance Learning

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