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Integrated animal monitoring system with animal detection and classification capabilities: a review on image modality, techniques, applications, and challenges

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Abstract

The continuous monitoring of animals is crucial for the well-being of both humans and animals. A comprehensive animal monitoring system must incorporate animal detection, classification, and deterrence techniques. This review paper addresses 8 research questions related to animal monitoring by presenting a comprehensive literature review of animal deterrence, monitoring, classification, and detection techniques. Additionally, it covers various animal image acquisition techniques, different image modalities, photogrammetry types, and unmanned vehicles used for animal studies. The paper also highlights the problems faced by animals and humans in co-existence and lists the challenges faced while capturing animal images in different modalities, such as visible, thermal, and aerial images. The conclusion includes a comparative study based on benchmark datasets and highlights future scope and areas that require further research in animal monitoring systems.

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Data availability

Data sharing does not apply to this article as no datasets were generated or analysed during the current study.

Abbreviations

AI:

Artificial intelligence

AVC:

Animal vehicle collision

CLBP:

Completed local binary pattern

CNN:

Convolutional neural network

CNT-HOG:

Contour based histogram of oriented gradients

CU:

Columbia dogs with parts

DCNN:

Deep convolutional neural network

DL:

Deep learning

DVC:

Deer vehicle collision

DWA:

Detection of moving wild animals algorithm

FCAN:

Fully convolutional attention network

FGC:

Fine grained classification

FRCNN:

Faster R-CNN

GAN:

Generative adversarial network

GIS:

Geographic information system

GPS:

Global positioning system

HAC:

Human animal conflict

HOG:

Histogram of oriented gradients

JPEG:

Joint photographic experts group

KDE:

Kernel density estimation

KFD:

Kernel fisher discriminator

KNN:

K nearest neighbour

LASSO:

Least absolute shrinkage and selection operator

LIDAR:

Light detection and ranging

ML:

Machine learning

MP-CNN:

Multi part-convolutional neural network

MSER:

Maximally stable extremal regions

NAC:

Neural activation constellations

OBIA:

Object-based image analysis

OX:

Oxford IIIT pet

PCA:

Principle component analysis

PIR:

Passive infrared

QA:

Quality assessment

ResNet:

Residual network

RFID:

Radio-frequency identification

RGB:

Red green blue

RNN:

Recurrent neural network

ScSPM:

Sparse coding spatial pyramid matching

SD:

Stanford dogs

SIFT:

Scale-invariant feature transform

SISURF:

SIFT–SURF (scale-invariant feature transform—speeded up robust features)

ISODATA:

Iterative self-organizing data analysis technique

SPV:

Selective pooling vectors

SS:

Snapshot serengeti

SSD:

Single shot multibox detector

SURF:

Speeded up robust features

SVM:

Support vector machine

TMBM:

Template matching binary mask

UAV:

Unmanned aerial vehicle

UGV:

Unmanned ground vehicle

VGGNet:

Visual geometry group network

VLIR:

Vertical-looking infrared

WILD:

Wildlife image and localization dataset

WTB:

Where’s the bear

YOLO:

You only look once

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  1. School of Social Science and Languages, Vellore Institute of Technology, Vellore, India

    N. Sundaram

  2. School of Computer Science and Engineering, VIT-AP University, Amaravathi, India

    S. Divya Meena

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Sundaram, N., Meena, S.D. Integrated animal monitoring system with animal detection and classification capabilities: a review on image modality, techniques, applications, and challenges. Artif Intell Rev 56 (Suppl 1), 1–51 (2023). https://doi.org/10.1007/s10462-023-10534-z

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