An active ensemble classifier for detecting animal sequences from global camera trap data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60077344%3A_____%2F25%3A00640091" target="_blank" >RIV/60077344:_____/25:00640091 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60076658:12310/25:43909870

Výsledek na webu

<a href="https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.70144" target="_blank" >https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.70144</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/2041-210X.70144" target="_blank" >10.1111/2041-210X.70144</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An active ensemble classifier for detecting animal sequences from global camera trap data

Popis výsledku v původním jazyce

Camera traps can generate huge amounts of images, and thus reliable methods for their automated processing are in high demand: in particular to find those images or image sequences that actually include animals. Automatically filtering out images that are empty or contain humans can be challenging, as images can be taken in different landscapes, habitats and light. Weather and seasonal conditions can vary greatly. Most of the images can be empty, because cameras using passive infrared sensors (PIR) trigger easily due to moving vegetation or rapidly varying shadows and sunny spots. Animals in images are often hiding behind vegetation, and camera traps will see them from previously unseen angles. Therefore, conventional animal image detection methods based on deep learning need huge training sets to achieve good accuracy. We present a novel background removal approach based on movement masked images computed using sequences of images. Our deep vision classifier uses these movement images for classification instead of the original images. Additionally, we apply a deep active learning (active learning for deep models) for collecting training samples to reduce the number of annotations required from the user. Our method performed well in singling out image sequences that actually include animals, thus filtering out the majority of images that were empty or contained humans. Most importantly, the method performed well also for backgrounds and animal species not seen in the training data. Active learning brought good separation between classes already with small training sets, without the need for laborious large-scale pre-annotation. We present a reliable and efficient method for filtering out empty image sequences and sequences containing humans. This greatly facilitates camera trapping research by enabling researchers to restrict the task of animal classification to only those image sequences that actually contain animals.

Název v anglickém jazyce

An active ensemble classifier for detecting animal sequences from global camera trap data

Popis výsledku anglicky

Camera traps can generate huge amounts of images, and thus reliable methods for their automated processing are in high demand: in particular to find those images or image sequences that actually include animals. Automatically filtering out images that are empty or contain humans can be challenging, as images can be taken in different landscapes, habitats and light. Weather and seasonal conditions can vary greatly. Most of the images can be empty, because cameras using passive infrared sensors (PIR) trigger easily due to moving vegetation or rapidly varying shadows and sunny spots. Animals in images are often hiding behind vegetation, and camera traps will see them from previously unseen angles. Therefore, conventional animal image detection methods based on deep learning need huge training sets to achieve good accuracy. We present a novel background removal approach based on movement masked images computed using sequences of images. Our deep vision classifier uses these movement images for classification instead of the original images. Additionally, we apply a deep active learning (active learning for deep models) for collecting training samples to reduce the number of annotations required from the user. Our method performed well in singling out image sequences that actually include animals, thus filtering out the majority of images that were empty or contained humans. Most importantly, the method performed well also for backgrounds and animal species not seen in the training data. Active learning brought good separation between classes already with small training sets, without the need for laborious large-scale pre-annotation. We present a reliable and efficient method for filtering out empty image sequences and sequences containing humans. This greatly facilitates camera trapping research by enabling researchers to restrict the task of animal classification to only those image sequences that actually contain animals.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

<a href="/cs/project/GM22-17593M" target="_blank" >GM22-17593M: Kolaps ekosystémů v nepřítomnosti pavouků a ptáků?</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Methods in Ecology and Evolution

ISSN

2041-210X

e-ISSN

2041-2096

Svazek periodika

16

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

2500-2516

Kód UT WoS článku

001590073300004

EID výsledku v databázi Scopus

2-s2.0-105014013917