Motion-based video compression for resource-constrained camera traps

• URL: http://arxiv.org/abs/2405.14419v2
• Date: Thu, 13 Jun 2024 23:48:05 GMT
• Title: Motion-based video compression for resource-constrained camera traps
• Authors: Malika Nisal Ratnayake, Lex Gallon, Adel N. Toosi, Alan Dorin,
• Abstract summary: We introduce a new motion analysis-based video compression algorithm designed to run on camera trap devices. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking. The methods outlined in this paper facilitate the broader application of computer vision-enabled, low-powered camera trap devices for remote, in-situ video-based animal motion monitoring.
• Score: 4.349838917565205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Field-captured video allows for detailed studies of spatiotemporal aspects of animal locomotion, decision-making, and environmental interactions. However, despite the affordability of data capture with mass-produced hardware, storage, processing, and transmission overheads pose a significant hurdle to acquiring high-resolution video from field-deployed camera traps. Therefore, efficient compression algorithms are crucial for monitoring with camera traps that have limited access to power, storage, and bandwidth. In this article, we introduce a new motion analysis-based video compression algorithm designed to run on camera trap devices. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing the overall data size by an average of 84% across a diverse set of test datasets while retaining the information necessary for relevant behavioural analysis. The methods outlined in this paper facilitate the broader application of computer vision-enabled, low-powered camera trap devices for remote, in-situ video-based animal motion monitoring.

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