eprintid: 10203021
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/20/30/21
datestamp: 2025-01-08 10:32:28
lastmod: 2025-01-08 10:32:28
status_changed: 2025-01-08 10:32:28
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Jenkins, Marcus
creators_name: Franklin, Kirsty A
creators_name: Nicoll, Malcolm AC
creators_name: Cole, Nik C
creators_name: Ruhomaun, Kevin
creators_name: Tatayah, Vikash
creators_name: Mackiewicz, Michal
title: Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring
ispublished: pub
divisions: UCL
divisions: B02
divisions: C08
divisions: D09
divisions: F99
keywords: YOLO; object detection; time-lapse imagery; camera-trap imagery; temporal features; spatio-temporal features; wildlife monitoring
note: © 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/).
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.
date: 2024
date_type: published
publisher: MDPI AG
official_url: https://doi.org/10.3390/s24248002
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2347030
doi: 10.3390/s24248002
lyricists_name: Nicoll, Malcolm
lyricists_id: MNICO77
actors_name: Nicoll, Malcolm
actors_id: MNICO77
actors_role: owner
full_text_status: public
publication: Sensors
volume: 24
number: 24
article_number: 8002
citation:        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 <https://doi.org/10.3390/s24248002>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10203021/1/Jenkins_2024.pdf