Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Miah, Mehdi"'
We propose a novel Transformer-based module to address the data association problem for multi-object tracking. From detections obtained by a pretrained detector, this module uses only coordinates from bounding boxes to estimate an affinity score betw
Externí odkaz:
http://arxiv.org/abs/2403.08018
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 meth
Externí odkaz:
http://arxiv.org/abs/2204.00423
We propose a method for multi-object tracking and segmentation based on a novel memory-based mechanism to associate tracklets. The proposed tracker, MeNToS, addresses particularly the long-term data association problem, when objects are not observabl
Externí odkaz:
http://arxiv.org/abs/2110.11284
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 introduce
Externí odkaz:
http://arxiv.org/abs/2107.07067
Publikováno v:
In Pattern Recognition April 2025 160
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 othe
Externí odkaz:
http://arxiv.org/abs/2010.07881