Rodent Arena Multi-View Monitor (RAMM): A Camera Synchronized Photographic Control System for Multi-View Rodent Monitoring
Although multi-view monitoring techniques have been widely applied in skinned model reconstruction and movement analysis, traditional systems using high-performance Personal Computers (PCs), or industrial cameras are often prohibitive due to high costs and limited scalability. Here, we introduce an affordable, scalable multi-view image acquisition system for skinned model reconstruction in animal studies, utilizing consumer Android devices and a wireless network for synchronized monitoring named Rodent Arena Multi-View Monitor (RAMM). It uses smartphones as camera nodes with local data storage, enabling cost-effective scalability. Its custom synchronization solution and portability make it ideal for research and education in rodent behavior analysis, offering a practical alternative for institutions with limited budgets. Furthermore, the portability and flexibility of this system make it an ideal tool for rodent skinned model research based on multi-view image acquisition. To evaluate the performance, we perform an oscilloscope analysis to ensure effectiveness of synchronization. A 45-camera node setup is built to highlight RAMM's cost efficiency and ease in constructing large-scale systems. Additionally, the data quality is validated using the Instant Neural Graphics Primitives (Instant-NGP) method. Remarkable results were achieved with a 30.49 dB PSNR by utilizing only 25 images with intrinsic and extrinsic parameters, fulfilling the requirements for well-synchronized data used in 3D representation algorithms.
Authors
- Yuxuan Qian (ORCID: https://orcid.org/0000-0002-0164-732X)
- Jianxin Wang (ORCID: https://orcid.org/0000-0003-1516-0480)
- Liu Bing-bin
Institutions
- Central South University (CN)
- Xiangya Hospital Central South University (CN)
Publication Details
- Journal
- Tsinghua Science & Technology
- Published
- 2025-04-29
- DOI
- https://doi.org/10.26599/tst.2024.9010117
- Citations
- 4
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 6.54