Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929643787911168 |
|---|---|
| author | Jenkins, Marcus Franklin, Kirsty A. Nicoll, Malcolm A. C. Cole, Nik C. Ruhomaun, Kevin Tatayah, Vikash Mackiewicz, Michal |
| author_facet | Jenkins, Marcus Franklin, Kirsty A. Nicoll, Malcolm A. C. Cole, Nik C. Ruhomaun, Kevin Tatayah, Vikash Mackiewicz, Michal |
| contents | Monitoring animal populations is crucial for assessing the health of ecosystems. Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data. In this paper, we show that the performance of an object detector in a single frame of a time-lapse sequence can be improved by including spatio-temporal features from the prior frames. We propose a method that leverages temporal information by integrating two additional spatial feature channels which capture stationary and non-stationary elements of the scene and consequently improve scene understanding and reduce the number of stationary false positives. The proposed technique achieves a significant improvement of 24\% in mean average precision (mAP@0.05:0.95) over the baseline (temporal feature-free, single frame) object detector on a large dataset of breeding tropical seabirds. We envisage our method will be widely applicable to other wildlife monitoring applications that use time-lapse imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16329 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring Jenkins, Marcus Franklin, Kirsty A. Nicoll, Malcolm A. C. Cole, Nik C. Ruhomaun, Kevin Tatayah, Vikash Mackiewicz, Michal Computer Vision and Pattern Recognition Artificial Intelligence 68T45 I.4.8 Monitoring animal populations is crucial for assessing the health of ecosystems. Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data. In this paper, we show that the performance of an object detector in a single frame of a time-lapse sequence can be improved by including spatio-temporal features from the prior frames. We propose a method that leverages temporal information by integrating two additional spatial feature channels which capture stationary and non-stationary elements of the scene and consequently improve scene understanding and reduce the number of stationary false positives. The proposed technique achieves a significant improvement of 24\% in mean average precision (mAP@0.05:0.95) over the baseline (temporal feature-free, single frame) object detector on a large dataset of breeding tropical seabirds. We envisage our method will be widely applicable to other wildlife monitoring applications that use time-lapse imaging. |
| title | Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T45 I.4.8 |
| url | https://arxiv.org/abs/2412.16329 |