Name
Title | ||
|---|---|---|
Online tracking of ants based on deep association metrics: method, dataset and evaluation |
Abstract | ||
|---|---|---|
•We introduce an online MOT framework to track ant individuals. This framework combines both motion and appearance matching, which effectively prevents trajectory fragments and ID switches from long-term occlusion caused by frequent interactions of ants, achieving efficient and high-precision tracking.•We obtain ant appearance features based on the ResNet model with cosine similarity metric, to track unlabeled ants for a long time in a fixed position camera. The experiments show that our method is successful and robust with only a small size (N = 50) of the training dataset, which makes it feasible to be applied in real applications with no need to construct a large training dataset.•We construct a dataset of ant tracking with a total size of 46091 samples. We built the dataset following the standard MOT formulation. In contrast to an extensive collection of human tracking datasets, there are few datasets of ant tracking which are publicly accessible. We believe this dataset will benefit future works with relevant research objectives. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patcog.2020.107233 | Pattern Recognition |
Keywords | DocType | Volume |
Ant tracking,ResNet model,Mahalanobis distance,Appearance descriptors | Journal | 103 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyan Cao | 1 | 0 | 0.34 |
| Shihui Guo | 2 | 44 | 15.97 |
| Juncong Lin | 3 | 105 | 20.73 |
| Wenshu Zhang | 4 | 0 | 0.34 |
| Minghong Liao | 5 | 90 | 18.97 |