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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.15518 |
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| _version_ | 1866917810207195136 |
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| author | Phan, Thinh Phillips, Isaac Lockett, Andrew Kidd, Michael T. Le, Ngan |
| author_facet | Phan, Thinh Phillips, Isaac Lockett, Andrew Kidd, Michael T. Le, Ngan |
| contents | Object tracking, especially animal tracking, is one of the key topics that attract a lot of attention due to its benefits of animal behavior understanding and monitoring. Recent state-of-the-art tracking methods are founded on deep learning architectures for object detection, appearance feature extraction and track association. Despite the good tracking performance, these methods are trained and evaluated on common objects such as human and cars. To perform on the animal, there is a need to create large datasets of different types in multiple conditions. The dataset construction comprises of data collection and data annotation. In this work, we put more focus on the latter task. Particularly, we renovate the well-known tool, LabelMe, so as to assist common user with or without in-depth knowledge about computer science to annotate the data with less effort. The new tool named as TrackMe inherits the simplicity, high compatibility with varied systems, minimal hardware requirement and convenient feature utilization from the predecessor. TrackMe is an upgraded version with essential features for multiple object tracking annotation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15518 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TrackMe:A Simple and Effective Multiple Object Tracking Annotation Tool Phan, Thinh Phillips, Isaac Lockett, Andrew Kidd, Michael T. Le, Ngan Computer Vision and Pattern Recognition Object tracking, especially animal tracking, is one of the key topics that attract a lot of attention due to its benefits of animal behavior understanding and monitoring. Recent state-of-the-art tracking methods are founded on deep learning architectures for object detection, appearance feature extraction and track association. Despite the good tracking performance, these methods are trained and evaluated on common objects such as human and cars. To perform on the animal, there is a need to create large datasets of different types in multiple conditions. The dataset construction comprises of data collection and data annotation. In this work, we put more focus on the latter task. Particularly, we renovate the well-known tool, LabelMe, so as to assist common user with or without in-depth knowledge about computer science to annotate the data with less effort. The new tool named as TrackMe inherits the simplicity, high compatibility with varied systems, minimal hardware requirement and convenient feature utilization from the predecessor. TrackMe is an upgraded version with essential features for multiple object tracking annotation. |
| title | TrackMe:A Simple and Effective Multiple Object Tracking Annotation Tool |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.15518 |