Mehdi Miah

Mehdi Miah
Polytechnique Montréal · Department of Computer and Software Engineering

Master Data Science

About

6
Publications
249
Reads
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7
Citations
Citations since 2016
6 Research Items
7 Citations
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Introduction
Hi ! I am a 4th year PhD student at LITIV advised by Guillaume-Alexandre Bilodeau and Nicolas Saunier. My research interests focus on computer vision, unsupervised learning and transportation.
Additional affiliations
January 2018 - April 2018
Polytechnique Montréal
Position
  • Intern
Education
September 2016 - October 2017
Université Paris-Saclay
Field of study
  • Data Science

Publications

Publications (6)
Preprint
Full-text available
This paper focuses on the detection of Parkinson's disease based on the analysis of a patient's gait. The growing popularity and success of Transformer networks in natural language processing and image recognition motivated us to develop a novel method for this problem based on an automatic features extraction via Transformers. The use of Transform...
Preprint
We propose a method for multi-object tracking and segmentation that does not require fine-tuning or per benchmark hyper-parameter selection. The proposed tracker, MeNToS, addresses particularly the data association problem. Indeed, the recently introduced HOTA metric, which has a better alignment with the human visual assessment by evenly balancing...
Preprint
Full-text available
We propose a method for multi-object tracking and segmentation (MOTS) that does not require fine-tuning or per benchmark hyperparameter selection. The proposed method addresses particularly the data association problem. Indeed, the recently introduced HOTA metric, that has a better alignment with the human visual assessment by evenly balancing dete...
Preprint
Full-text available
This paper addresses the problem of selecting appearance features for multiple object tracking (MOT) in urban scenes. Over the years, a large number of features has been used for MOT. However, it is not clear whether some of them are better than others. Commonly used features are color histograms, histograms of oriented gradients, deep features fro...

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