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 and Mackiewicz, Michal ORCID: https://orcid.org/0000-0002-8777-8880 (2024) Improving object detection for time-lapse imagery using temporal features in wildlife monitoring. Sensors, 24 (24). ISSN 1424-8220

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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.

Item Type:	Article
Additional Information:	Data Availability Statement: The dataset presented in this article is not readily available because it is part of an ongoing multi-partner collaborative study. Requests to access the datasets should be directed to Malcolm Nicoll (malcolm.nicoll@ioz.ac.uk). Funding information: This research received no external funding.
Uncontrolled Keywords:	3* ,/dk/atira/pure/researchoutput/REFrank/3_
Faculty \ School:	Faculty of Science > School of Biological Sciences Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Colour and Imaging Lab
Depositing User:	LivePure Connector
Date Deposited:	20 Dec 2024 01:13
Last Modified:	20 Dec 2024 01:14
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98052
DOI:	10.3390/s24248002

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