TY  - JOUR
A1  - Jenkins, Marcus
A1  - Franklin, Kirsty A
A1  - Nicoll, Malcolm AC
A1  - Cole, Nik C
A1  - Ruhomaun, Kevin
A1  - Tatayah, Vikash
A1  - Mackiewicz, Michal
PB  - MDPI AG
JF  - Sensors
KW  - YOLO; object detection; time-lapse imagery; camera-trap imagery; temporal features; spatio-temporal features; wildlife monitoring
VL  - 24
N1  - © 2024 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
N2  - 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.
IS  - 24
UR  - https://doi.org/10.3390/s24248002
ID  - discovery10203021
Y1  - 2024///
TI  - Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring
AV  - public
ER  -