A motion-based compression algorithm for resource-constrained video camera traps

[2405.14419] A motion-based compression algorithm for resource-constrained video camera traps Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2405.14419 (cs) [Submitted on 23 May 2024 ( v1 ), last revised 2 Oct 2024 (this version, v3)] Title: A motion-based compression algorithm for resource-constrained video camera traps Authors: Malika Nisal Ratnayake , Lex Gallon , Adel N. Toosi , Alan Dorin View PDF HTML (experimental) Abstract: Field-captured video facilitates detailed studies of spatio-temporal aspects of animal locomotion, decision-making and environmental interactions including predator-prey relationships and habitat utilisation. But even though data capture is cheap with mass-produced hardware, storage, processing and transmission overheads provide a hurdle to acquisition of high resolution video from field-situated edge computing devices. Efficient compression algorithms are therefore essential if monitoring is to be conducted on single-board computers in situations where such hurdles must be overcome. Animal motion tracking in the field has unique characteristics that necessitate the use of novel video compression techniques, which may be underexplored or unsuitable in other contexts. In this article, we therefore introduce a new motion analysis-based video compression algorithm specifically designed for camera traps. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking on three popular edge computing platforms. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing overall data size by an average of 87% across diverse test datasets. Our experiments demonstrate the algorithm's capability to preserve critical information for insect behaviour analysis through both manual observation and automatic analysis of the compressed footage. The method presented in this paper enhances the applicability of low-powered computer vision edge devices to remote, in situ animal motion monitoring, and improves the efficiency of playback during behavioural analyses. Our new software, EcoMotionZip, is available Open Access. Comments: 17 pages, 6 figures, 3 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM) Cite as: arXiv:2405.14419 [cs.CV] (or arXiv:2405.14419v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2405.14419 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1007/s11263-026-02803-5 Focus to learn more DOI(s) linking to related resources Submission history From: Malika Nisal Ratnayake [ view email ] [v1] Thu, 23 May 2024 10:39:33 UTC (13,616 KB) [v2] Thu, 13 Jun 2024 23:48:05 UTC (13,608 KB) [v3] Wed, 2 Oct 2024 00:07:34 UTC (22,919 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2024-05 Change to browse by: cs cs.AI q-bio q-bio.QM References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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