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Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring

Jenkins, Marcus; Franklin, Kirsty A; Nicoll, Malcolm AC; Cole, Nik C; Ruhomaun, Kevin; Tatayah, Vikash; Mackiewicz, Michal; (2024) Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring. Sensors , 24 (24) , Article 8002. 10.3390/s24248002. Green open access

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Abstract

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.

Type: Article
Title: Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/s24248002
Publisher version: https://doi.org/10.3390/s24248002
Language: English
Additional information: © 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/).
Keywords: YOLO; object detection; time-lapse imagery; camera-trap imagery; temporal features; spatio-temporal features; wildlife monitoring
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
URI: https://discovery.ucl.ac.uk/id/eprint/10203021
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