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Table 1 Papers related to insect tracking

From: Detecting and tracking honeybees in 3D at the beehive entrance using stereo vision

Reference

Targets

Loc/obs

Cam/fps

Detection/likehood method

Tracking method

[8]

< 15 ants

2D/2D

1/30

ABS

Basic GNN

[19]

< 20 ants

2D/2D

1/30

Appearance model

MRF-augmented FP

[15]

1 bee*

2D/2D

1/15

Eigenbee (PCA)

PF

[16]

1 bee*

2D/2D

1/15

Adaptive appearance model (e.g., color image)

PF supported by a behavior model

[17]

1 bee*

2D/2D

1/15

Weighted adaptive appearance model (e.g., color image)

Idem + geometric constraints

[13]

< 100 bees

2D/2D

1/30

Vector quantization (VQ)

Overlapping ellipse

[18]

< 100 bees

2D/2D

1/14

Tag detection (method not mentioned)

Mean shift

[11]

1 bee

2D/2D

1/?

Viola-Jones detector

Combined NN classification of BOF

[9]

n bees

3D/2D

1/30

ABS + ellipse matching

Basic GNN

[10]

1 bee

3D/2D

1/24

ABS

NN

[14]

< 100 bats

3D/3D

3 IR/125

Direct linear transform

MHT

Our

< 25 bees

3D/3D

2/60

Hybrid intensity/depth segmentation

GNN and 3D (re)projection

  1. Summary of recent papers related to insect and animal tracking. NN, near neighbor; BOF, bag of feature; ABS, adaptive background subtraction; PF, particle filter; KF, Kalman filter; GNN, Global Near Neighbor; IR, infrared; PCA, principal component analysis; MHT, multi-hypothesis tracker. *Not explicitly mentioned in the paper but possibly extensible to several targets.