Domain Generalization for Activity Recognition: Learn from Visible,
Infer with Thermal
Yannick Zoetgnande
and Jean Louis Dillenseger
Independent Researcher, France
Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
Fall Detection, Activity Recognition, Domain Generalization, Thermal Images.
We proposed a solution based on I3D and optical flow to learn common characteristics between thermal
and visible videos. For this purpose we proposed a new database to evaluate our solution. The new model
comprises an optical flow extractor; a feature extractor based on I3D, a domain classifier, and an activity
recognition classifier. We learn invariant characteristics computed from the optical flow. We have simulated
several source domains, and we have shown that it is possible to obtain excellent results on a modality that
was not used during the training. Such techniques can be used when there is only one source and one target
Nowadays, in most countries, the population is be-
coming older and older. Aging is one of the main
reasons for frailty (Ambrose et al., 2013). As pointed
by (Khan and Hoey, 2017), there are 2.6 falls per per-
son per year in retirement homes. And given the in-
creasing number of seniors and the limited number of
retirement homes, some seniors must stay at home.
Thus, fall detection will concern domestic context
than retirement homes. To prevent falls, it is neces-
sary to survey seniors for a long time. But there are a
lot of issues regarding this surveillance, such as non-
invasive and privacy.
Vision-based sensors have proven to be very effi-
cient while being passive. Many works were focused
on visible images (RGB). But this type of image poses
a huge problem of privacy intrusion. Some other vi-
sion sensors (depth or thermal sensors) do not allow
explicit recognition of people and are therefore rele-
vant for activity monitoring while respecting privacy.
Thermal sensors have many other advantages; they
work day and night, are easier to analyze than nor-
mal images in daily activities classification (Vadivelu
et al., 2016); but are generally very expensive. To
reduce this cost, some authors proposed to use low-
cost thermal cameras, but with very low resolution
(80x60 pixels), of type Lepton 2 from FLIR (Zoet-
gnande et al., 2020; Zoetgnand
e et al., 2019).
The problem that arises then is how to use this
type of image in the context of deep learning. Indeed,
if there is a lot of work in this field, most people have
mainly focused on RGB or depth (usually Kinect)
sensors. Thus, many comprehensive databases are
available for these modalities. On the other hand,
only a few data exist for thermal images and even
less for low-resolution ones. The main dataset on
thermal images are (Vadivelu et al., 2016), (Pe
Asturiano et al., 2018) and (Mart
nor et al.,
2019). In (Vadivelu et al., 2016), the authors provide
a dataset captured from FLIR ONE thermal cameras.
The resolution of images was 640×480. In (Pe
Asturiano et al., 2018), the authors acquired a dataset
named FALL-UP by combining 6 infrared sensors, 2
normal cameras, 1 EEG, and 5 wearable sensors. In
nor et al., 2019), the authors provide
a dataset named UP-Fall by combining 6 infrared sen-
sors, 2 cameras, 5 IMUs, a gyroscope, ambient light,
and 1 EEG. But even they are using thermal cameras,
they only provided the thermal images features and
no raw data.
In the literature, activity recognition domain gen-
eralization is most of the time focused on IXMAS
(Weinland et al., 2006). This dataset has been widely
used as a cross-view action recognition benchmark
(Li et al., 2018; Li et al., 2019). The videos have been
collected from five different views. We think that gen-
eralization through different views does not demon-
strate perfectly the performance of a domain gener-
alization algorithm. In this dataset, different persons
Zoetgnande, Y. and Dillenseger, J.
Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal.
DOI: 10.5220/0010906300003122
In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 722-729
ISBN: 978-989-758-549-4; ISSN: 2184-4313
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
perform the same actions in different ways, so it is
common that a model might not be able to recognize
actions performed by new subjects not seen during
training. Also, we cannot expect a model trained us-
ing indoor data to work well outdoors. This is why in
this paper, we propose that the source and the target
model do not share either the same persons, either the
same views and even more neither the same modality
(visible spectrum vs. thermal).
This paper will then try to answer the following
question: is it possible to learn from classical modal-
ity datasets (e.g., RGB) and infer thermal data. The
main idea is then to train some networks on data col-
lected and labeled for a fairly complete study of activ-
ity recognition from RGB images (Tran et al., 2018b)
and verify the accuracy of the inference on a much
smaller database of low-resolution thermal images.
To our best knowledge, it is the first time domain gen-
eralization is performed from visible to thermal. The
source code is open
Our contributions are threefold:
This paper fills a gap in the literature on fall de-
tection and activity recognition with thermal cam-
eras. Indeed, most of the previous works learn and
infer on the same database. In this paper, we pro-
pose to go further.
We proposed to use different modalities. We
proposed a new model for domain generalization
from visible videos to thermal videos.
We proposed a new toy activity recognition/fall
detection dataset with thermal images. Even if
this new dataset is relatively small compared to
other state-of-the-art datasets, it allows us to prove
the efficiency of our method.
2.1 Activity Recognition Network
Like in many other fields, methods based on deep
learning have got outstanding results in activity clas-
sification. Most of these methods have been designed
for visible or depth videos classification. In (Carreira
and Zisserman, 2017), the authors propose I3D a two-
stream architecture based on the image classification
inception architecture (Szegedy et al., 2016).
Most of the time, it is not easy to find the right
architecture. A neural architecture search can tackle
The code is at
ICPRAM submission
such a problem. In (Piergiovanni et al., 2019), the
authors propose a hybrid architecture based on in-
flated Temporal Gaussian Mixture (iTGM) based on
ResNet (He et al., 2016). The iTGM is based on the
1D Temporal Gaussian Mixture (TGM) proposed in
(Piergiovanni and Ryoo, 2019). Some parameters of
their ResNet are fixed while others evolve; thus, the
search space is limited. In (Ryoo et al., 2019) the au-
thors propose a similar approach by evolving a multi-
stream neural network. Their network is composed
of a convolution block by alternating 2D and (2+1)D
residual modules.
While I3D-based approaches are accurate but
computationally costly, in (Tran et al., 2018a), Tran
et al. propose to separate spatial and temporal com-
ponents in a new spatiotemporal convolutional block
named R(2+1)D based on ResNet architecture. Like
in (Piergiovanni et al., 2019), the authors use a 2D
convolution for spatial dimension and a 1D convolu-
tion for temporal dimension. They show that this ar-
chitecture, based on Factorized Spatio-temporal con-
volutional Networks (Wang et al., 2017), is easier to
optimize than 3D convolutions. Technically they re-
place ν
3D convolutional filters of size (ν
× t ×
d × d) with µ
(2+1)D blocks composed of µ
2D con-
volutional filters of size (ν
× 1 × d × d) and ν
1D convolutional filters of size (µ
× 1 × d × d).
They choose µ
. They show that their
ResNet-based approach beats many state-of-the-art
methods with lower computational complexity. As
a conclusion of this review of deep learning activ-
ity recognition methods, we believe that I3D and
R(2+1)D architectures are the most suited for our
2.2 Domain Generalization
2.2.1 Visible Domain and Related
One of the problems we faced for thermal-based ac-
tivity recognition is the scarcity of datasets. Origi-
nal domain generalization is used to adapt a model
trained with a dataset into another dataset. Domain
generalization is linked to domain adaptation and
few-shot learning. But while in these techniques,
the model sees the target data during training, in do-
main generalization, the model does not see the target
dataset during training.
The main reason why we want to apply domain
generalization to our problem is that data acquisition
takes time and annotation is time-consuming. Train-
ing a model using RGB video and inferring on Ther-
mal videos will save us time. Indeed, there is a
Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal
huge number of datasets for RGB images, but ther-
mal datasets are sparser.
A jth domain is defined as
,X ,Y ) where X and Y are the set of data inputs
and labels. Generally we have m sources domains.
The main idea of domain generalization is to learn to
classify a data from unseen domain d
/ D
with D
the set of available domain during training.
They are many ways to perform domain general-
ization: domain invariant features, hierarchical mod-
els, data augmentation and optimization algorithms.
We will be more focused about domain invariant
features. The main idea is to learn a representation Φ
such that P(Φ(x
)) is the same whatever the domain.
In (Albuquerque et al., 2019), the authors also pro-
pose to make training distributions indistinguishable.
They define a domain as
D, f
where D is the prob-
ability distribution over X and f the deterministic la-
beling function defined as f : X Y . They also de-
fine h H with H a set of candidate hypothesis with
f : X Y .
A risk R or source error can associated to h on
D, f
as follows (Ben-David et al., 2010):
R[h] = E
L[h(x), f (x)] (1)
with L : Y × Y R
representing how different
h(x) and f (x).
2.2.2 From Visible to Thermal
In the literature, there are some works regarding
visible-to-transfer learning. In (Akkaya et al., 2021),
the authors proposed a domain adaptation model
with three phases pre-training, warm-up, and pseudo-
labeling. Their proposed model is composed of three
modules: Source CNN, Target CNN, Classifier, and
In (Liu et al., 2018), the author proposes a work
that is close to ours. They extract features using iDTs
(Wang and Schmid, 2013) + LLC + PCA method.
Then they used kernel manifold alignment to create a
common latent space of thermal and visible features.
Finally, aligned-to-generalized encoders are used to
classify activities. Their method is more related to
transfer learning given that the model is trained with
visible and thermal video, while we want to only train
the model with visible video and infer with thermal
2.3 Activity Monitoring Dataset
Regarding data availability, there is a lot of data for
human activity recognition (Carreira et al., 2018; Car-
reira et al., 2019). Unfortunately, most of these
datasets concern visible images. In the specific case
of monitoring human activities as a goal of fall de-
tection, the literature is also prolific regarding visible
image (Charfi et al., 2012) and recently about Kinect
images (Adhikari et al., 2017).
In (Tran et al., 2018b), the authors propose a fall
detection dataset with 8 falls types, 50 participants (20
women and 30 men). To collect the dataset, they use
7 overlapped Kinect and visible cameras and 2 WAX3
wireless accelerometers. We used this dataset as the
source and collected a new dataset as the target.
There are a few dataset using thermal images. In
nor et al., 2019), the authors pro-
pose UP-Fall a fall detection dataset composed of
255 videos. They used 17 participants (8 men and
9 women). To acquire the dataset, they use 6 in-
frared sensors, 2 visible cameras, 5 IMUs with an
accelerometer, a gyroscope, an ambient light, and 1
In (Pe
nafort-Asturiano et al., 2018), the authors
propose a new fall detection dataset composed of 255
videos. This dataset is acquired using 6 infrared sen-
sors, 2 cameras, 1 EEG, 5 wearable inertial sensors.
3.1 Dataset
Given that the dataset available in the state of the
art was not satisfactory, we decided to collect a new
dataset for thermal activity recognition. This is one
of the core contributions of our paper. To acquire
the images, we use Lepton 2.5 FLIR cameras (Fig 1).
In the Table 1, there are the specifications of lepton
2.5. These cameras are characterized by low resolu-
tion, noise, sudden brightness change (due to clutter
refreshment), and halo effect. The main advantage is
that these cameras are low-cost, thus can be used for
industrial applications or for anyone wanting to install
them in their homes.
We used two cameras set in stereo to take advan-
tage of the depth estimation. We set the stereo base-
line to 16 cm for a tradeoff between congestion and
the field of the view of the cameras.
We collected 713 videos following the same ac-
tions described in (Tran et al., 2018b): walk, hand
pick up, fall, crawl, sit then stand up, sit then fall, lie
then sit up and lie then fall. Contrary to many other
datasets in literature, our dataset has been acquired
in real apartment situations. These rooms were fur-
nished and therefore presented many occlusions to the
cameras. Moreover, these data have been acquired for
various room temperatures.
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
Someone might suggest that our dataset is not big
enough. Indeed, compared to other works in the lit-
erature we do not have enough data. However, we
do not have the same objectives as these works. In-
deed we place ourselves in the situation where a per-
son could train a model a big dataset and deploy this
model. So knowing that the performance of his model
will not decrease even if the inference dataset is dif-
ferent from the training dataset.
Someone might suggest that our database is not
large enough and does not contain enough views to do
domain generalization. Compared for example to the
IXMAX database (which is the standard database for
domain generalization in activity detection), we have
enough videos. Indeed, authors usually use only the
first 5 actions (compared to our 8 actions). Moreover,
they usually use 6 people (Alba, Andreas, Daniel,
Hedlena, Julien and Nicolas) not taking into account
irregular actions.
Figure 1: The device we used to capture the images. We
registered the two views but used only the left views in this
paper. In future work, we will include the second view.
Table 1: Characteristics of Lepton 2.5.
Dimension 8.5 x 11.7 x 5.6 mm
Resolution 80 (h) x 60 (v) pixels
Pixel size 17 µm
Field of view 51
(h) x 37.83
Thermal sensitivity <50 mK
Accuracy ± 5
Frame rate 9 Hz
Dynamic range -10 to 140
Price <200$
3.2 Domain Generalization
One solution could be to train directly using the
dataset we have collected for fine-tuning. Unfortu-
nately, we considered that we didn’t have enough
videos (713 videos) to evaluate. This is why we pro-
posed to learn from visible videos and infer what we
have learned from thermal videos.
Table 2: Number of videos per class for our dataset Baga.
action id action name Number of videos
1 walk 91
2 hand pick up 91
3 lie then sit 81
4 sit then stand up 91
5 crawl 91
6 lie then fall 81
7 sit then fall 91
8 fall 91
3.2.1 Source Dataset
For source dataset, we use the dataset CMDFALL
provided by (Tran et al., 2018b). We choose this
dataset because it involves 50 people and seven view-
points with 20 activities. We modified the dataset for
our purpose. First, they use 20 activities with some
activities such as right fall and left fall. We have
grouped with the activities that only differ in the sense
that they have been performed left or right. Besides,
we did not consider one of the cameras because it was
placed on the ceiling.
Moreover, given that their frame rate is 20 fps, we
temporally down-sampled the video to 5 fps. As a
result, we delete videos where the activities lasted less
than 3 seconds. In the end, we got 6235 videos with
some imbalance between classes (Table 3).
Table 3: Number of videos per class for the original cleaned
dataset CMDFALL (Tran et al., 2018b).
acton id action name number of videos
1 walk 1972
2 hand pick up 319
3 lie then sit 311
4 sit then stand up 1017
5 crawl 277
6 lie then fall 834
7 sit then fall 537
8 fall 958
3.2.2 Data Augmentation
As shown in Table 3, one of the problems we faced
is that the cleaned CMDFALL dataset is imbalanced.
There are many solutions to learn from imbalanced
dataset (Johnson and Khoshgoftaar, 2019): data-level
methods, algorithm-level methods, and hybrid meth-
ods. We chose to apply the data-level method through
over-sampling with additive data augmentations in or-
der to reach 1972 videos per class.
The main problem of oversampling is over-fitting.
Thus, we had a random affine transformation and ran-
Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal
(a) N
12. (b) N
13. (c) N
Figure 2: Baga dataset: frames during a fall from a bed.
dom occlusion to any video besides traditional data-
augmentation methods. The main idea is to be able to
get an infinite number of videos from one video. The
additive data augmentation techniques are described
as follows:
Random Occlusion. Randomly generate a rectan-
gle with a random size r
× r
with r
[0,m] and
[0,n]. The rectangle is randomly moved during
the video.
Random Affine Transformation. Given a video
v =
,· ·· , I
with a frame size of m × n. For a
given video a transformation matrix is randomly gen-
erated and applied to the frames.
3.2.3 From One Source to Three Sources
To perform domain generalization, it is possible to
consider one source domain and one target domain
(Qiao et al., 2020). In this work, we decide to cre-
ate artificially three source domains from one source.
Given a visible RGB video (source 0), we simulated
two more sources by computing Sobel edges (source
1) and Laplacian edges (source 2).
3.2.4 Domain Generalization Model
Figure 3: Domain generalization model (Model
For domain generalization, we adapted the model pro-
posed by (Albuquerque et al., 2019). Our model,
called Model
m is composed of a pretrained flownet
model to extract optical flow, a feature extractor based
on I3D model, a task classifier, and domain classifiers.
Our main idea is to make the distributions we
created indistinguishable. A domain is defined as
D, f
where D is the probability distribution over X
and f the deterministic labeling function defined as
f : X Y . They also define h H with H a set of
candidate hypothesis with f : X Y .
A source error h is defined on domain
D, f
R[h] = E
L[h(x), f (x)] (2)
Thus the H -divergence is defined as follows:
] = 2sup
[η(x) = 1] Pr
[η(x) = 1]
While in domain adaptation, there is only one
source and one target, in domain generalization, there
are many sources and one target domain. Using a
one-vs-all classification allows reducing the number
of combinations and the complexity of the model.
Given a video input vid, our domain generaliza-
tion model is composed of 4 modules (Fig 3):
1. A optical flow module OF(vid) that computes the
optical flow online. We used flownet2. The pa-
rameters θ
of this module are frozen.
2. A feature extraction E (with parameters θ
) mod-
ule must be able to extract features that must be
invariant in terms of action classification and dis-
similar in terms of domain classification.
3. A task classifier C (with parameters θ
) that is
trained to classify E(OF(vid)) in terms of activi-
4. Domain classifiers D
that is trained to classify
E(OF(vid)) in terms of domains.
The loss function of the model is defined as fol-
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
(C(E(x; θ
where y
is the activity label
0,1, 2,3, 4,5,6, 7
of x, and y
is the domain
label that is to say y
= 1 if x D
,0 otherwise. L
is the error between the predicted activity and the
ground truth activity while L
is the error between
the predicted domain and the ground truth domain.
4.1 Settings
4.1.1 Dataset
As aforementioned, we used two types of datasets. A
visible dataset was provided in the literature that we
cleaned and a thermal dataset we collected Table 2.
The frame rate of the two datasets is set to 5fps and
the resolution is 80x60. Each activity lasts at least 3
seconds and for training (and testing) 15 frames are
randomly selected. In Fig 2, some example of Baga
dataset are displayed. These frames represent a per-
son falling from bed.
Regarding the source dataset, videos have been
originally recorded from 50 subjects. We use the first
40 persons for training and the remaining 10 subjects
from testing. The source and the target datasets can be
downloaded through this link
4.1.2 Training Settings
We use a pretrained I3D backbone on the Kinetics
dataset. The domain classifier is composed of two
fully connected layers, followed by a ReLU and a
dropout. There is also a last fully connected layer, fol-
lowed by a softmax layer. The task classifier is com-
posed of an average pooling followed by a dropout,
a 3D convolutional layer, and a softmax layer. The
model is trained using stochastic gradient descent for
20 epochs, and the initial learning rates are 0.01 for
both the task and the domain classifier.
We evaluated three models:
1. Baseline: This model is composed of the tradi-
tional training. Here, we followed the procedure
presented in (Carreira and Zisserman, 2017). We
first extracted the optical flow using TVL1 and
then the model is trained normally without the do-
main generalization. That is to say this model is
composed only of E and C of Fig 3. We used tra-
ditional data augmentation techniques: flipping,
rotation and random cropping. In this model we
used optical flow of visible videos as source and
optical flow of thermal videos as target. The op-
tical flow data can be download through this link
2. Model
: This model is composed of the module
OF, E and C. In this model we used optical flow
of visible videos as source and optical flow of
thermal videos as target. OF is the optical flow
model which is a pretrained version of flownet2.
E is the features extractor and C the classifier.
3. Model
: This model is composed of the module
OF, E, C and D
s but during training we used
traditional data augmentation techniques and the
data augmentation techniques we proposed in this
4.2 Results
We reported the results of two models compared to
the baseline method. In terms of accuracy and F1
score (Table 4), it is easily noticeable that the baseline
is not able to categorize videos. Indeed this simple
model cannot generalize to a new domain even with
the same type of input (i.e. optical flow). Model
similar to the baseline method except that the optical
flow is calculated differently. Even if we are using the
same input (optical flow) as for Model
, the model
cannot generalize to an unseen domain without the
discriminator modules. Indeed we got 60.45% of ac-
curacy and 59.68% of F1-score, which is better than
the baseline from a big margin. These results are not
good compared to those of Model
. Indeed, the model
we proposed output 72.93% of accuracy and 71.56%
of F1-score. So Table 4 shows that using domain gen-
eralization, we can learn activities from a domain to
another domain through inferring.
The figures 4 and 5 support this idea. In these fig-
ures, we computed the confusion matrices for Model
and Model
. While Model
is able to classify some
activities such as fall, pick and fall but fails to classify
lie sit.
Both models (Model
, Model
) struggle to clas-
sify well the action lie sit. Model
tends to confound
this activity with fall. In order to improve the results,
we could include the second view of our device Fig 1.
Domain Generalization for Activity Recognition: Learn from Visible, Infer with Thermal
Table 4: Accuracy/F1 score of the evaluated methods.
Model Accuracy/F1 score %
Baseline 44.50/42.56
Model1 60.45/59.68
Model2 72.93/71.56
Figure 4: Results on the test dataset with Model
In this paper, we presented a new dataset for fall de-
tection and activity recognition. The dataset has been
acquired in real apartments contrary to many datasets
in the literature.
Then, we proposed a domain generalization model
for transfer learning. Rather than learning directly
from raw video, we proposed a model based on op-
tical flow. We showed that using flownet to extract
optical flow from simulated three sources (raw video,
Sobel videos, and Laplacian videos) we were able to
obtain better results compared to the baseline method
and Model
We wished to acquire more data and compared the
domain generalization method vs. domain adaptation
and fine-tuning.
It has also been proven in the literature that multi-
view frameworks can improve results. Adding more
views should bring more information to the model and
increase the performance.
Figure 5: Results on the test dataset with Model
This work was part of the PRuDENCE project (ANR-
16-CE19-0015) which has been supported by the
French National Research Agency (ANR).
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