8 citations · 10 across the 5 of their papers we have counts for
5 papers
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…
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…
Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots
Eyad Shaklab, Areg Karapetyan, Arjun Sharma +5
In addition to its crucial impact on customer satisfaction, last-mile delivery (LMD) is notorious for being the most time-consuming and costly stage of the shipping process. Pressi…
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…