Honeybee Re-identification in Video: New Datasets and Impact of
Jeffrey Chan
, Hector Carri
, R
emi M
, Jos
e L. Agosto Rivera
and Tugrul Giray
Department of Mathematics, University of Puerto Rico, R
ıo Piedras Campus, Puerto Rico
Department of Computer Science, University of Puerto Rico, R
ıo Piedras Campus, Puerto Rico
Department of Biology, University of Puerto Rico, R
ıo Piedras Campus, Puerto Rico
Re-identification, Contrastive Learning, Self-supervised Learning, Animal Monitoring.
This paper presents an experimental study of long-term re-identification of honeybees from the appearance of
their abdomen in videos. The first contribution is composed of two image datasets of single honeybees ex-
tracted from 12 days of video and annotated with information about their identity on long-term and short-term
scales. The long-term dataset contains 8,962 images associated to 181 known identities and used to evaluate
the long-term re-identification of individuals. The short-term dataset contains 109,654 images associated to
4,949 short-term tracks that provide multiple views of an individual suitable for self-supervised training. A
deep convolutional network was trained to map an image of the honeybee’s abdomen to a 128 dimensional fea-
ture vector using several approaches. Re-identification was evaluated in test setups that capture different levels
of difficulty: from the same hour to a different day. The results show using the short-term self-supervised in-
formation for training performed better than the supervised long-term dataset, with best performance achieved
by using both. Ablation studies show the impact of the quantity of data used in training as well as the impact
of augmentation, which will guide the design of future systems for individual identification.
The United Nations estimated that around 1 mil-
lion animals and plants are threatened with extinc-
tion causing a dangerous decline of species (UN Press
material, 2019). Active monitoring of endangered
species can prevent extinction by the early detection
of threats and studies of survival behaviors. Cur-
rent monitoring systems are categorized as intrusive
(Boenisch et al., 2018; M
egret et al., 2019) and
non-intrusive (Bozek et al., 2021; Romero-Ferrero
et al., 2019). Intrusive monitoring involves attach-
ing a marker to individuals to reduce monitoring to
the detection and identification of markers. This ap-
proach simplifies the analysis, but is restricted to con-
trolled environments with access to the individuals in
advance to perform marking. It present the advantage
of providing individualized analysis of behavior pat-
terns, which provide much finer grained information
for detailed assessment of animal health, social be-
havior and division of labour amongst others. On the
other hand, non-intrusive monitoring consists of plac-
ing a camera trap without any marker. In this case,
the detection and tracking can then be performed us-
ing computer vision algorithms, leaving identification
to a set of experts if the number of events detected
is small enough. Manual identification is an arduous
and time-consuming task, which then require automa-
tion for large time spans or if many individuals are to
be monitored, which is the case for honeybees.
Recently, (Romero-Ferrero et al., 2019) developed
a method for markerless tracking of groups of an-
imals in laboratory conditions where the animal al-
ways stays in the camera field of view. They trained
a re-identification model using tracking information
to incrementally build appearance models of each in-
dividual to solve ambiguities during crossings. An
incremental approach to build individual appearance
models from initial partial trajectories was used in
(Bozek et al., 2021) to solve track interruptions from
images of honeybees inside an observation colony,
this time relaxing the constraint of fixed number of
individuals. Re-identifying animals in their natural
habitat is particularly challenging because an individ-
ual can decide to go out of the field of view for an
indefinite amount of time. For this reason, it requires
robust models to connect tracks with a significant time
gap in between and undefined number of individuals.
Chan, J., Carrión, H., Mégret, R., Rivera, J. and Giray, T.
Honeybee Re-identification in Video: New Datasets and Impact of Self-supervision.
DOI: 10.5220/0010843100003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP, pages
ISBN: 978-989-758-555-5; ISSN: 2184-4321
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Figure 1: General architecture of the creation of the short-term and long-term re-identification datasets. A) Pose-based detec-
tor detects head, neck, waist, and abdomen tip and performs short-term tracking based on the waist. B) Angle compensated
body extraction of untagged bees. C) Decode tags using AprilTag and body extraction with angle compensation of tagged
bees. D) Filter detections using rules based on abdomen size, abdomen angle, and closest bee. E) Abdomen extraction,
remove the upper body to avoid data leaks from tagged images. G) Long-term training dataset where the identity class is the
tag id. H) Short-term training dataset where the identity class is the track id.
Using contrastive learning, (Schneider et al.,
2020) achieved re-identification performance beyond
humans capabilities on datasets of tigers, fruit fly,
monkeys, and whales. One of the key to success
in this case is the large number of images annotated
with their groundtruth identity, which where carefully
obtained from experts. Unfortunately, such manual
labeling of identities is much more challenging for
some species, such as honeybees, due to the large
number of individuals and the lack of human experts
that can perform this task.
Capturing the variations of appearance of the same
individual can also be obtained using detection and
tracking algorithms, but this is limited to monitor ani-
mals while the individual stays in the camera’s field of
view. This can capture short-term variations by asso-
ciating contiguous instances to the same identity and
learn invariance to pose changes, rotation, and defor-
Because of the difficulty of collecting large-scale
data with supervised identity annotation, we inves-
tigate in this paper how to leverage the short-term
tracking information as self-supervised training infor-
mation and evaluate its impact on the performance in
long-term re-identification of honeybees.
The paper is organized as follows. In section 2,
we review the related work in terms of methodol-
ogy and application of re-identification to animals. In
section 3, we present the design and building of two
honeybee re-identification datasets that will be shared
with to the community and that will be used for a de-
tailed evaluation of performances. In section 4, we
introduce the models, training and evaluation proce-
dures. In section 5, we show the experimental re-
sults and discuss their implications for the develop-
ment of improved re-identification approaches, before
concluding in section 6.
2.1 Animal Re-Identification
Human Re-identification is a well-known task in the
field of computer vision. The community had been
very active for years thanks to the availability of mas-
sive labeled datasets such as Market-1501 (Zheng
et al., 2015), and CUHK03 (Li et al., 2014) that en-
ables the development of specialized methods for hu-
man re-identification. Unfortunately, the animal re-
identification community had not been able to reach
the same performance. A major factor is the avail-
ability of identity annotated datasets. Animal datasets
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Table 1: Organization and statistics of the contributed datasets.
Dataset Split # individuals # images # tracks
Mean images
per tracks
Mean tracks
per id
short-term train - 109,654 4,949 22.15 -
long-term train 181 3,777 801 4.71 4.42
valid 66 1,909 309 6.17 4.68
test 126 3,276 696 4.70 5.52
have their unique characteristics, animals do not wear
clothes or makeup, but individuals may look very sim-
ilar to each other, making the annotation an arduous
and time-consuming task that even sometimes is un-
feasible for experts.
Although animal datasets are hard to collect, re-
cent efforts have led to the collection of medium-
size datasets for species such as tigers (Li et al.,
2019), elephants (K
orschens et al., 2018), cattle (Gao
et al., 2021; Bergamini et al., 2018), and primates
(Deb et al., 2018; Brust et al., 2017; Schofield et al.,
2019). Most of these datasets have focused on super-
vised learning by involving experts to label the iden-
tity of the individuals. For species with a large num-
ber of individuals such as honeybees, data collection
can capture hundreds or even thousands of identities
quickly, but annotation cannot be performed by ex-
perts. Therefore, this work explores self-supervision
toward a re-identification of honeybees.
Recently the Cows2021 dataset (Gao et al., 2021)
used tracking and self-supervised learning to help to
annotate more individuals for the dataset based on
their color patterns. The authors used triplet loss,
sampling the positive pair from the same track, and
the negative example from a different video to train an
invariant feature space from 301 short videos, reach-
ing 0.57 ID accuracy on a different test set with 182
2.2 Self-supervised Learning
The success of a deep model on a visual task depends
on learning suitable features for the downstream task,
such as image classification, object detection, or re-
identification. Pretraining had become a crucial com-
ponent of the model training to achieve state-of-the-
art performance. In pretraining, the model is opti-
mized to perform a similar task to learn initial rele-
vant features before fine-tuning with the downstream
task. This similar task may benefit from a massive
annotated dataset to train the network, such as the Im-
ageNet dataset. For fine-grained tasks where a mas-
sive dataset is not available self-supervised learning
is used. Self-supervised learning aims to pre-train
a network with a pretext task that does not require
manually annotated labels (Misra and Maaten, 2020;
Noroozi and Favaro, 2016; Chen et al., 2020a). Sim-
CLRv2 had been shown to outperforms standard su-
pervised training, even when fine-tuning with only
10% of the labels (Chen et al., 2020b). These results
motivate the approach we propose, where we com-
bine data augmentation and tracking as a generator
for pseudo labels to learn visual features relevant to
honeybees’ re-identification.
The purpose of this dataset is to evaluate the re-
identification of unmarked bees on a long-term setup
using two training modalities: 1) short-term training
dataset which captures an individual in a short period;
and 2) long-term training dataset which has annota-
tions of individuals on long-term period. Figure 1
shows an overview of the pipeline for the extraction
of both short-term and long-term datasets.
3.1 Extraction of Individual Images
The raw video data was collected using a camera at
the entrance of a colony recording honeybees’ activ-
ities over multiple weeks. The videos were recorded
at 20 fps, with quality of 1440x2560 pixels from July
17-24 and August 1-4 from 8 am to 6 pm. A sub-
set of the honeybees was tagged several days prior to
recording to ensure they would perform foraging trips
and be visible in the monitored entrance. A paper tag
was attached to their body, containing unique April-
Tag barcodes (Wang and Olson, 2016). The data col-
lection of tagged bees is scarce and requires delicate
manipulation of the honeybees. Meanwhile, the data
collection of untagged bees is automatic and massive
as it only depends on individuals to appear in the cam-
era field of view.
For all videos, the bee pose estimator (Rodriguez
et al., 2018) was used to detect the skeleton. We
used a modified skeleton template that includes the
head, neck, waist, and abdomen tip. The annotations
to train the pose estimation model were made using
the SLEAP annotation interface (Pereira et al., 2020)
to annotate 98 frames from one-hour video manually.
The waist keypoint is used as a reference point for
tracking, which is performed with a Hungarian al-
Honeybee Re-identification in Video: New Datasets and Impact of Self-supervision
Figure 2: Example images of the raw dataset. Some images
exhibit abdomen curling, crowded images and occlusions
that were filtered out based on pose detection data.
gorithm. The neck and waist key points are used to
compute the angle to normalize the body in the im-
age extraction. Figure 1A shows an example of pose-
based detection and tracking of tagged and untagged
bees. Tags are detected in each frame, and the tag id is
greedily assigned to a body detection based on a min-
imum distance below 50 pixels between the tag and
the neck. Although the entire bodies were extracted
for both datasets, as shown in Figure 4, the identity
appearance models only used a cropped image of the
abdomen to avoid any data leak: the tag image was
is used only as the source of the groundtruth informa-
tion and was removed completely from the data used
in the study (see Figure 1E).
One challenge on bee identification is the ab-
domen curling that deforms the abdomen skin pat-
tern, making the re-identification much more difficult.
Other difficult samples exhibit occlusion and crowded
images. Figure 2 show images from the raw dataset
with examples of curling, occlusion, and crowded im-
ages. In this work, prospect instances where the indi-
vidual has a curled abdomen were filtered out based
on abdomen angle and size. Occluded bodies and
crowded images were also excluded by removing the
detections for which the distance from the waist key-
point to other bee waist was less than 300 pixels.
3.2 Short-term Dataset
The short-term dataset will be used solely for training
purposes. The short-term dataset is based on track-
lets of individuals. Each tracklet tracks the individ-
ual while its stays in the field of view of the cam-
era. This data collection pipeline allows capturing a
massive number of individuals in a short period. All
the tracks were collected on the first 10 minutes of
each hour from July 17 to July 24. All videos were
downsampled to 10 fps. After the filtering based on
abdomen size and angle, only tracks containing more
than 10 images were kept.
This dataset contains 4,949 tracks; each track has
an average of 22.15 images for a total of 109,654 im-
ages. On average, the length of a tracklet is about 5
seconds. The tracks were annotated with their track
ID, meaning that different tracks are considered as
different individuals for the triplet loss. We rely on
the expectation that very few triplets will incorrectly
select the same individual for the negative sample due
to a large number of individuals. The examples in Fig-
ure 4a show that this dataset captures small variations
mainly in the pose and illumination.
The short-term dataset was split randomly at track
level into 80% training and 20% validation. It was not
used for evaluation.
3.3 Long-term Dataset
The long-term dataset will be used both for training
and evaluation purposes. The long-term dataset con-
sists of marked bees that are monitored using barcode
tags. This tagging allows monitoring the individuals
on a long-term period. Due to physical limitations
marking bees is limited to a few hundred individuals.
The dataset is split by July 17–23, July 24, and
August 1– 4 for the training, validation, and testing
set respectively, as shown in Figure 3. The long-term
dataset ignores detections that do not have a tag as-
sociated. The training split contains 181 individuals
on 801 tracks with a total of 3,777 images. The vali-
dation split contains 66 individuals on 309 tracks for
a total of 1,909 images. The test split contains 126
individuals on 696 tracks for a total of 3,276 images.
The test set has 29 identities that overlap the training
set. The examples in Figure 4b show that this dataset
captures drastic variations such as illumination, pose,
and wings overlap.
4.1 Embedding Network Architecture
The embedding network is a custom convolutional
neural network (CNN) that takes the RGB image crop
of the abdomen of the honeybee and outputs a 128
dimensional feature vector. Figure 5 shows the gen-
eral architecture of the network. It consists of a 7x7
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Figure 3: Number of tracks per day showing the split of the
long-term dataset into training, validation and testing.
convolution layer with ReLU activation followed by
3 ResNet full pre-activation residual units (He et al.,
2016), each with two 3 × 3 convolutions and output
dimension of 64. The head of the network consists of
a fully connected layer with an output of 128 dimen-
sions. During training, dropout is applied before and
after the fully connected layer with a probability of
0.5 and 0.2 respectively. The output of the network is
L2 normalized.
Re-ID is performed by comparing the embedding
of a query image to all embeddings of gallery images
and ranking by Euclidean distance.
4.2 Training Protocols
Our training protocols consist of three modalities:
1) Fully Supervised (long-term), 2) Self-supervised
(short-term), and 3) Supervised + Pretraining (short-
term + long-term).
The fully supervised protocol uses the long-term
training dataset to minimize the distance of im-
ages of the same individual at different tracks.
The self-supervised protocol uses the short-term
dataset to minimize the distance of images on the
same track. On the self-supervised protocol, the
tracks were annotated with their track ID, mean-
ing that different tracks are considered as differ-
ent individuals for the triplet loss. We rely on the
expectation that very few triplets will incorrectly
select the same individual for the negative sample
due to a large number of individuals.
The supervised + pretraining protocol pre-trains
the same as self-supervised protocol and finetunes
the network with the fully supervised protocol.
The objective function for the three protocols is
the Semi-Hard Triplet Loss with a margin of 0.2. Op-
timization is performed using Adam with a learning
rate of 0.001 for 1000 epochs using early stop with a
patience of 100 epochs. Data augmentation included:
color distortion, color drop, gaussian blur and random
4.3 Evaluation Setups
The evaluation were always performed in the same
way, independently from the training protocol, using
only the test part of the long-term dataset, which pro-
vides ground-truth Re-ID information from the tags.
It is based on a set of queries that are each compared
to a gallery composed of one positive sample and 10
distractors. All of the queries and galleries are sam-
pled randomly from the long-term test dataset under
three scenarios of increasing difficulty: 1) same day,
same hour; 2) different day, same hour of the day; 3)
different day, any hour.
Pairs of tracks with the same ID were generated
from all tracks on the long-term test dataset with ad-
ditional conditions specific to each setup: 1) the same
day-same hour protocol selects track pairs that are at
least 15 minutes apart but not more than 60 minutes;
2) the diff day-same hour setup selects track pair that
are on different days, but for which the time of the
day is less than 60 minutes apart; 3) the diff day-any
hour setup only enforces that the tracks are on dif-
ferent days. For each track in the pair, an image is
randomly sampled from the track, and this process is
repeated 100 times per track pair to generate an im-
age query and its associated positive image sample.
For each query, the 10 image distractors were sampled
randomly from all negative IDs. The number of track
pairs used for evaluation were 379, 236, and 1518 for
the setups 1, 2, 3 respectively.
We report performance using the Cumulative
Matching Characteristics (CMC) metric on rank-1
and rank-3, which represent the average rate at which
a positive sample is ranked within the closest 1 (resp.
3) to its query.
In this section, we will evaluate the 3 training proto-
cols in the 3 evaluation setups, and study the effect of
both augmentation and amount of training data on the
5.1 Base Performance
Table 2 shows that the short-term protocol outper-
forms the long-term protocol in all evaluation setups,
by significant margins: +0.209 in same day, same
hour, +0.188 in different day same hour, +0.136 in
different day. This suggests that the short-term dataset
Honeybee Re-identification in Video: New Datasets and Impact of Self-supervision
(a) (b)
Figure 4: Example of pre-aligned images for the (a) Short-term and (b) Long-term datasets. The full image is shown to
provide context, although only the highlighted abdomen area is used for training and evaluation, to ensure the tag that serves
as groundtruth is not used by the model. Each row contains 4 images of the same individual.
7 × 7 × 32 conv
ResNet Residual Unit
with two 3 × 3 × 64 conv
ResNet Residual Unit
with two 3 × 3 × 64 conv
ResNet Residual Unit
with two 3 × 3 × 64 conv
Dropout p=0.5
FC 128
Dropout p=0.2
L2 Normalization
Figure 5: Model Architecture. The ResNet units are all full pre-activation residual units following 2× 2 polling.
captures variations relevant for re-identification in
all cases, although its advantage reduces with more
challenging evaluation setups. Both approaches are
outperformed by the protocol using short-term pre-
training with long-term fine-tuning, which provides
an improvement of: +0.021, +0.072 and +0.088 re-
spectively over the short-term protocol itself in the ap-
proach with augmentation.
Table 2 shows that data augmentation improves
the performance considerably in the short-term and
the long-term training protocols. It has a consistent
negative effect on the short-term + long-term training
5.2 Effect of Amount of Training Data
Although the long-term dataset has identities annota-
tion over a long time span, previous sections showed
it was at a disadvantage compared to the short-term
dataset. We hypothesize a major factor is the lower
amount of data. The long-term training data is signif-
icantly more challenging to obtain, as it requires ex-
tensive marking of individuals with barcode markers,
which leads to a lower amount of unique individuals
and lower amount of data usable for training. This
section investigates how the amount of tracks affect
the performance of the short-term training protocol to
identify the trade-off between the quantity of data and
its time scale.
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Table 2: Cumulative Matching Characteristic performance of the three training protocols on the three evaluation setups. For
each training protocol, performance is evaluated without and with augmentation, and the difference shown on the third row.
Same day, same hour Diff day, same hour Diff day, any hour
Rank 1 Rank 3 Rank 1 Rank 3 Rank 1 Rank 3
Triplet loss, No Aug
0.456 0.733 0.322 0.610 0.273 0.557
Triplet loss, Aug 0.529 0.781 0.391 0.709 0.362 0.664
Triplet loss, No Aug
0.682 0.888 0.508 0.775 0.436 0.720
Triplet loss, Aug 0.738 0.912 0.579 0.831 0.498 0.788
Triplet Loss, No Aug Short-term
+ Long-term
0.801 0.932 0.659 0.889 0.624 0.868
Triplet Loss, Aug 0.759 0.913 0.651 0.880 0.586 0.832
Figure 6: Effect of amount of training data on performance
(CMC, rank 1). Marker shape represents the type of training
(bullet for short-term vs crosses for long-term training data).
Color represents the type of evaluation (blue for same day-
same hour, red for diff day-same hour, green for diff day-
any hour). Horizontal axis represents multiplicative factor
from baseline of 181 tracks in short-term dataset, and 181
unique identities in long-term dataset.
For this experiment, the number of tracks in the
short-term dataset was reduced to match the num-
ber of identities in the long-term dataset and create
a baseline. As expected this baseline performs worst
than long term, meaning that where the short-term
and long-term have the same amount of identities the
long-term dataset is much better. Figure 6 and Table 3
shows that the performance increases linearly with re-
spect to the log of the number of tracks. When more
data is available the performance increase up to the
point where the short-term outperform the long-term
as is shown in Figure 3. Due to the difficulty to gather
more long-term training data, only the 1X factor is
shown, which is limited by the number of marked
bees. Collecting more short-term training data only
requires processing more video in an unsupervised
way. It should be investigated in future work at which
point the performance increase from such additional
data would start tapering off.
Animal re-identification is a challenging problem due
to the lack of large-scale annotated datasets to both
learn relevant models and evaluate performance in
detail. In this paper, we proposed two main ap-
proaches to make progress. First, we contributed two
large image datasets of honeybees. Both where ex-
tracted by leveraging automatic detection, pose esti-
mation and tracking of honeybees from multiple days
of video. All images have been compensated for posi-
tion and orientation, as to provide well aligned images
of honeybee bodies and their abdomen. The long-
term dataset was annotated with the identity of 181
individuals recognized using barcode tags on their
thorax, spanning up to 12 days. The second dataset
was annotated based on short-term tracks IDs, which
didn’t provide individual IDs, but could be collected
at a larger scale than the long-term dataset (more than
12x the number of images).
The approach considered relies on contrastive
learning with triplet-loss to train a 128 dimensions
identity feature vector suitable for re-identification.
The experimental study of the performance of re-
identification showed the critical impact of the
amount of data and the importance of data augmen-
tation to maximize the performance. The results indi-
cate that automated short-term tracking is a good ap-
proach to obtain the large amount of data required to
learn re-identification models with limited human in-
tervention. Although it cannot capture all possible de-
grees of variation a single individual may exhibit over
long periods of time, it still capture relevant variations
when enough data is collected, and ultimately outper-
forms training with the long-term dataset, which is
more limited in terms of number of unique individuals
due to the necessity of tagging physically the individ-
uals to generate the groundtruth. Best performance
was obtained by combining the two datasets, using
the short-term data for pre-training and the long-term
data for fine-tuning the Re-ID network.
Honeybee Re-identification in Video: New Datasets and Impact of Self-supervision
Table 3: Effect of the amount of training data in the short-term training protocol with triplet loss and augmentation on CMC
performance metric at rank-1 and rank-3.
# tracks
Same day, same hour Diff day, same hour Diff day, any hour
Rank 1 Rank 3 Rank 1 Rank 3 Rank 1 Rank 3
181 0.183 0.397 0.112 0.322 0.106 0.305
362 0.315 0.578 0.239 0.463 0.186 0.408
724 0.392 0.676 0.249 0.534 0.251 0.520
1448 0.548 0.795 0.422 0.688 0.378 0.655
2896 0.663 0.884 0.504 0.774 0.421 0.725
4949 0.680 0.885 0.538 0.814 0.480 0.761
These results show the possibility to recognize
honeybees amongst a gallery of distractors over multi-
ple days using only images of their abdomen. Future
work will consider how the performance of such an
approach would be improved with lightweight mark-
ings such as paint, by considering full-body images
and by further increasing the scale of automatically
collected training datasets, which could yield practi-
cal ways to track larger number of individuals over
multiple hours and days without heavy marking pro-
This work is supported by grant no. 2021-67014-
34999 from the USDA National Institute of Food
and Agriculture. This material is based upon work
supported by the National Science Foundation under
grants no. 1707355 and 1633184. J. C. acknowledges
support from the PR-LSAMP Bridge to the Doctor-
ate, a program from the NSF under award number
HRD-1906130. T. G. acknowledges NSF-HRD award
#1736019 that provided funds for the purchase of
bees. This work used the High-Performance Comput-
ing facility (HPCf) of the University of Puerto Rico,
supported by National Institute of General Medical
Sciences, National Institutes of Health (NIH) award
number P20GM103475 and NSF grants number EPS-
1002410 and EPS-1010094. This work used the Ex-
treme Science and Engineering Discovery Environ-
ment (XSEDE), which is supported by National Sci-
ence Foundation grant number ACI-1548562. Specif-
ically, it used the Bridges-2 system, which is sup-
ported by NSF award number ACI-1928147, at the
Pittsburgh Supercomputing Center (PSC).
Bergamini, L., Porrello, A., Dondona, A. C., Negro, E. D.,
Mattioli, M., D’alterio, N., and Calderara, S. (2018).
Multi-views Embedding for Cattle Re-identification.
In 2018 14th International Conference on Signal-
Image Technology & Internet-Based Systems (SITIS),
pages 184–191.
Boenisch, F., Rosemann, B., Wild, B., Dormagen, D.,
Wario, F., and Landgraf, T. (2018). Tracking All
Members of a Honey Bee Colony Over Their Lifetime
Using Learned Models of Correspondence. Frontiers
in Robotics and AI, 5:35.
Bozek, K., Hebert, L., Portugal, Y., and Stephens, G. J.
(2021). Markerless tracking of an entire honey bee
colony. Nature Communications, 12(1):1733.
Brust, C.-A., Burghardt, T., Groenenberg, M., Kading, C.,
Kuhl, H. S., Manguette, M. L., and Denzler, J. (2017).
Towards automated visual monitoring of individual
gorillas in the wild. In Proceedings of the IEEE Inter-
national Conference on Computer Vision Workshops,
pages 2820–2830.
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G.
(2020a). A simple framework for contrastive learn-
ing of visual representations. Proceedings of the
37th International Conference on Machine Learning,
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hin-
ton, G. E. (2020b). Big Self-Supervised Models are
Strong Semi-Supervised Learners. In Larochelle, H.,
Ranzato, M., Hadsell, R., Balcan, M. F., and Lin, H.,
editors, Advances in Neural Information Processing
Systems, volume 33, pages 22243–22255. Curran As-
sociates, Inc.
Deb, D., Wiper, S., Gong, S., Shi, Y., Tymoszek, C.,
Fletcher, A., and Jain, A. K. (2018). Face recogni-
tion: Primates in the wild. In 2018 IEEE 9th Interna-
tional Conference on Biometrics Theory, Applications
and Systems (BTAS), pages 1–10. IEEE.
Gao, J., Burghardt, T., Andrew, W., Dowsey, A. W.,
and Campbell, N. W. (2021). Towards Self-
Supervision for Video Identification of Individual
Holstein-Friesian Cattle: The Cows2021 Dataset.
arXiv preprint arXiv:2105.01938.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Iden-
tity Mappings in Deep Residual Networks BT - Com-
puter Vision ECCV 2016. pages 630–645, Cham.
Springer International Publishing.
orschens, M., Barz, B., and Denzler, J. (2018). Towards
automatic identification of elephants in the wild. arXiv
preprint arXiv:1812.04418.
Li, S., Li, J., Lin, W., and Tang, H. (2019). Amur
tiger re-identification in the wild. arXiv preprint
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Li, W., Zhao, R., Xiao, T., and Wang, X. (2014). DeepReID:
Deep Filter Pairing Neural Network for Person Re-
identification. In 2014 IEEE Conference on Computer
Vision and Pattern Recognition, pages 152–159.
egret, R., Rodriguez, I. F., Ford, I. C., Acu
na, E., Agosto-
Rivera, J. L., and Giray, T. (2019). LabelBee: A Web
Platform for Large-Scale Semi-Automated Analysis
of Honeybee Behavior from Video. In Proceedings of
the Conference on Artificial Intelligence for Data Dis-
covery and Reuse, AIDR ’19, New York, NY, USA.
Association for Computing Machinery.
Misra, I. and Maaten, L. v. d. (2020). Self-supervised learn-
ing of pretext-invariant representations. In Proceed-
ings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition, pages 6707–6717.
Noroozi, M. and Favaro, P. (2016). Unsupervised learn-
ing of visual representations by solving jigsaw puz-
zles. In Lecture Notes in Computer Science (includ-
ing subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), volume 9910
LNCS, pages 69–84.
Pereira, T. D., Tabris, N., Li, J., Ravindranath, S., Papadoy-
annis, E. S., Wang, Z. Y., Turner, D. M., McKenzie-
Smith, G., Kocher, S. D., Falkner, A. L., Shaevitz,
J. W., and Murthy, M. (2020). SLEAP: Multi-animal
pose tracking. bioRxiv, page 2020.08.31.276246.
Rodriguez, I. F., M
egret, R., Egnor, R., Branson, K.,
Agosto, J., Giray, T., and Acuna, E. (2018). Multi-
ple animals tracking in video using part affinity fields.
In Workshop on visual observation and analysis of
vertebrate and insect behavior. In Proceedings of the
24th International Conference on Pattern Recognition
(ICPR), Beijing, China, pages 20–24.
Romero-Ferrero, F., Bergomi, M. G., Hinz, R. C., Heras,
F. J. H., and de Polavieja, G. G. (2019). idtracker.ai:
tracking all individuals in small or large collectives of
unmarked animals. Nature Methods, 16(2):179–182.
Schneider, S., Taylor, G. W., and Kremer, S. C. (2020).
Similarity learning networks for animal individual re-
identification-beyond the capabilities of a human ob-
server. In Proceedings of the IEEE Winter Conference
on Applications of Computer Vision Workshops, pages
Schofield, D., Nagrani, A., Zisserman, A., Hayashi,
M., Matsuzawa, T., Biro, D., and Carvalho, S.
(2019). Chimpanzee face recognition from videos
in the wild using deep learning. Science Advances,
UN Press material (2019). UN Report: Nature’s Dangerous
Decline ’Unprecedented’; Species Extinction Rates
’Accelerating’. UN Sustainable Development Goals,
page 19.
Wang, J. and Olson, E. (2016). AprilTag 2: Efficient and
robust fiducial detection. In 2016 IEEE/RSJ Interna-
tional Conference on Intelligent Robots and Systems
(IROS), pages 4193–4198.
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and
Tian, Q. (2015). Scalable Person Re-identification: A
Benchmark. In 2015 IEEE International Conference
on Computer Vision (ICCV), pages 1116–1124.
Honeybee Re-identification in Video: New Datasets and Impact of Self-supervision