Motion vectors and deep neural networks for video camera traps

Riechmann, Miklas; Gardiner, Ross; Waddington, Kai; Rueger, Ryan; Leymarie, Frederic Fol and Rueger, Stefan. 2022. Motion vectors and deep neural networks for video camera traps. Ecological Informatics, 69, 101657. ISSN 1574-9541 [Article]

1-s2.0-S1574954122001066-main.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview
[img] Text (Plain Text Bibliography)
bibliography.txt - Bibliography

Download (8kB)

Abstract or Description

Commercial camera traps are usually triggered by a Passive Infra-Red (PIR) motion sensor necessitating a delay between triggering and the image being captured. This often seriously limits the ability to record images of small and fast moving animals. It also results in many “empty” images, e.g., owing to moving foliage against a background of different temperature. In this paper we detail a new triggering mechanism based solely on the camera sensor. This is intended for use by citizen scientists and for deployment on an affordable, compact, low-power Raspberry Pi computer (RPi). Our system introduces a video frame filtering pipeline consisting of movement and image-based processing. This makes use of Machine Learning (ML) feasible on a live camera stream on an RPi. We describe our free and open-source software implementation of the system; introduce a suitable ecology efficiency measure that mediates between specificity and recall; provide ground-truth for a video clip collection from camera traps; and evaluate the effectiveness of our system thoroughly. Overall, our video camera trap turns out to be robust and effective.

Item Type:


Identification Number (DOI):

Additional Information:

This research is supported in part by the project COS4CLOUD, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 863463. COS4CLOUD is a project coordinated by the Institut de Ciències del Mar ICM-CSIC, based in Barcelona (Spain).


AI, Computer vision, Machine learning, Motion vectors, Video camera trap, Video pipeline

Departments, Centres and Research Units:



July 2022Published
18 May 2022Published Online
25 April 2022Accepted
9 September 2021Submitted

Item ID:


Date Deposited:

23 Jun 2022 15:41

Last Modified:

23 Jun 2022 15:41

Peer Reviewed:

Yes, this version has been peer-reviewed.


View statistics for this item...

Edit Record Edit Record (login required)