32 citations · 45 across the 14 of their papers we have counts for
6 papers · 1 filter
Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking
Mohamed Nagy, Naoufel Werghi, Jorge Dias +1
The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation. However, these descriptors are…
Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking
Mohamed Nagy, Naoufel Werghi, Bilal Hassan +2
Robust 3D state estimation in multi-object tracking is crucial for object localization in self-driving vehicles. Misalignment between the Kalman filter (KF) motion model and an obj…
EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving
Nadya Abdel Madjid, Murad Mebrahtu, Abdulrahman Ahmad +5
This paper introduces the Emirates Multi-Task (EMT) dataset, designed to support multi-task benchmarking within a unified framework. It comprises over 30,000 frames from a dash-cam…
RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud
Mohamed Nagy, Naoufel Werghi, Bilal Hassan +2
This paper addresses limitations in 3D tracking-by-detection methods, particularly in identifying legitimate trajectories and reducing state estimation drift in Kalman filters. Exi…
DFR-FastMOT: Detection Failure Resistant Tracker for Fast Multi-Object Tracking Based on Sensor Fusion
Mohamed Nagy, Majid Khonji, Jorge Dias +1
Persistent multi-object tracking (MOT) allows autonomous vehicles to navigate safely in highly dynamic environments. One of the well-known challenges in MOT is object occlusion whe…
Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuning
Christopher J. Holder, Majid Khonji, Jorge Dias +1
A major challenge in machine learning is resilience to out-of-distribution data, that is data that exists outside of the distribution of a model's training data. Training is often…