Literature DB >> 35498233

Digital video recorder for Raspberry PI cameras with multi-camera synchronous acquisition.

Ghadi Salem1, Jonathan Krynitsky1, Noah Cubert1, Alex Pu2, Simeon Anfinrud1, Jonathan Pedersen1, Joshua Lehman1, Ajith Kanuri1, Thomas Pohida1.   

Abstract

Video acquisition and analysis have become integral parts of scientific research. Two major components of a video acquisition system are the choice of camera and the acquisition software. A vast variety of cameras are available on the market. Turnkey multi-camera synchronous acquisition software, however, is not as widely available. For prototyping applications, the Raspberry Pi (RPi) has been widely utilized due to many factors, including cost. There are implementations for video acquisition and preview from a single RPi camera, including one implementation released by the RPi organization itself. However, there are no multi-camera acquisition solutions for the RPi. This paper presents an open-source digital video recorder (DVR) system for the popular RPi camera. The DVR is simple to setup and use for acquisition with a single camera or multiple cameras. In the case of multiple cameras, the acquisition is synchronized between cameras. The DVR comes with a graphical user interface (GUI) to allow previewing the camera streams, setting recording parameters, and associating "names" to cameras. The acquisition code as well as the DVR GUI are written in Python. The open-source software also includes a GUI for playback of recorded video. The versatility of the DVR is demonstrated with a life science research application involving high-throughput monitoring of fruit-flies.

Entities:  

Keywords:  Digital Video Recorder (DVR); Multi-camera synchronized acquisition; Raspberry PI camera DVR

Year:  2020        PMID: 35498233      PMCID: PMC9041262          DOI: 10.1016/j.ohx.2020.e00160

Source DB:  PubMed          Journal:  HardwareX        ISSN: 2468-0672


  5 in total

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Authors:  Alice A Robie; Kelly M Seagraves; S E Roian Egnor; Kristin Branson
Journal:  J Exp Biol       Date:  2017-01-01       Impact factor: 3.312

2.  Three-Dimensional Pose Estimation for Laboratory Mouse From Monocular Images.

Authors:  Ghadi Salem; Jonathan Krynitsky; Monson Hayes; Thomas Pohida; Xavier Burgos-Artizzu
Journal:  IEEE Trans Image Process       Date:  2019-04-01       Impact factor: 10.856

Review 3.  Automated image-based tracking and its application in ecology.

Authors:  Anthony I Dell; John A Bender; Kristin Branson; Iain D Couzin; Gonzalo G de Polavieja; Lucas P J J Noldus; Alfonso Pérez-Escudero; Pietro Perona; Andrew D Straw; Martin Wikelski; Ulrich Brose
Journal:  Trends Ecol Evol       Date:  2014-06-05       Impact factor: 17.712

4.  Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

Authors:  Weizhe Hong; Ann Kennedy; Xavier P Burgos-Artizzu; Moriel Zelikowsky; Santiago G Navonne; Pietro Perona; David J Anderson
Journal:  Proc Natl Acad Sci U S A       Date:  2015-09-09       Impact factor: 11.205

5.  SCORHE: a novel and practical approach to video monitoring of laboratory mice housed in vivarium cage racks.

Authors:  Ghadi H Salem; John U Dennis; Jonathan Krynitsky; Marcial Garmendia-Cedillos; Kanchan Swaroop; James D Malley; Sinisa Pajevic; Liron Abuhatzira; Michael Bustin; Jean-Pierre Gillet; Michael M Gottesman; James B Mitchell; Thomas J Pohida
Journal:  Behav Res Methods       Date:  2015-03
  5 in total

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