Gcnnmatch: Graph convolutional neural networks for multi-object tracking via sinkhorn normalization

I Papakis, A Sarkar, A Karpatne - arXiv preprint arXiv:2010.00067, 2020 - arxiv.org
This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph
Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature …
被引用次数：68 相关文章

GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization

I Papakis, A Sarkar, A Karpatne - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph
Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature …
[PDF] researchgate.net

[PDF][PDF] GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization

I Papakis, A Sarkar, A Karpatne - researchgate.net
This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph
Convolutional Neural Network (GCNN) based feature extraction and end-toend feature …
[PDF] researchgate.net

[PDF][PDF] GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization

I Papakis, A Sarkar, A Karpatne - researchgate.net
This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph
Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature …