A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929265698668544 |
|---|---|
| author | Yang, Cheng-Yen Huang, Hsiang-Wei Jiang, Zhongyu Wang, Hao Wallace, Farron Hwang, Jenq-Neng |
| author_facet | Yang, Cheng-Yen Huang, Hsiang-Wei Jiang, Zhongyu Wang, Hao Wallace, Farron Hwang, Jenq-Neng |
| contents | Dense object counting or crowd counting has come a long way thanks to the recent development in the vision community. However, indiscernible object counting, which aims to count the number of targets that are blended with respect to their surroundings, has been a challenge. Image-based object counting datasets have been the mainstream of the current publicly available datasets. Therefore, we propose a large-scale dataset called YoutubeFish-35, which contains a total of 35 sequences of high-definition videos with high frame-per-second and more than 150,000 annotated center points across a selected variety of scenes. For benchmarking purposes, we select three mainstream methods for dense object counting and carefully evaluate them on the newly collected dataset. We propose TransVidCount, a new strong baseline that combines density and regression branches along the temporal domain in a unified framework and can effectively tackle indiscernible object counting with state-of-the-art performance on YoutubeFish-35 dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03461 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video Yang, Cheng-Yen Huang, Hsiang-Wei Jiang, Zhongyu Wang, Hao Wallace, Farron Hwang, Jenq-Neng Computer Vision and Pattern Recognition Dense object counting or crowd counting has come a long way thanks to the recent development in the vision community. However, indiscernible object counting, which aims to count the number of targets that are blended with respect to their surroundings, has been a challenge. Image-based object counting datasets have been the mainstream of the current publicly available datasets. Therefore, we propose a large-scale dataset called YoutubeFish-35, which contains a total of 35 sequences of high-definition videos with high frame-per-second and more than 150,000 annotated center points across a selected variety of scenes. For benchmarking purposes, we select three mainstream methods for dense object counting and carefully evaluate them on the newly collected dataset. We propose TransVidCount, a new strong baseline that combines density and regression branches along the temporal domain in a unified framework and can effectively tackle indiscernible object counting with state-of-the-art performance on YoutubeFish-35 dataset. |
| title | A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.03461 |