Semi-supervised Anomaly Detection for Weakly-annotated Videos
Khaled El-Tahan
and Marwan Torki
Computer and Systems Engineering Department, Alexandria University, Egypt
Semi-supervision, Pseudo Labels, Weak-supervision, Multiple Instance Learning, Anomaly Detection,
Background Subtraction, Video Recognition.
One of the significant challenges in surveillance anomaly detection research is the scarcity of surveillance
datasets satisfying specific ethical and logistical requirements during the collection process. Weakly super-
vised models aim to solve those challenges by only weakly annotating surveillance videos and creating so-
phisticated learning techniques to optimize these models, such as Multiple Instance Learning (MIL), which
maximizes the boundary between the most anomalous video clip and the least normal (false alarm) video clip
using ranking loss. However, maximizing the boundary does not necessarily assign each clip its correct class.
We propose a semi-supervision technique that creates pseudo labels for each correct class. Also, we investigate
different video recognition models for better features representation. We evaluate our work on the UCF-Crime
(Weakly Supervised) dataset and show that it almost outperforms all other approaches by only using the same
simple baseline (multilayer perceptron neural network). Moreover, we incorporate different evaluation metrics
to show that not only did our solution increase the AUC, but it also increased the top-1 accuracy drastically.
Almost all public places now rely on surveillance
cameras to increase public safety. However, the hu-
man need for surveillance analysis is very high in de-
mand and very costly. The need for automatic surveil-
lance anomaly detection systems is now higher than
ever. The challenge with surveillance anomaly de-
tection models is the dataset availability; many eth-
ical and logistical requirements prevent us from col-
lecting private surveillance videos. We have to rely
on the publicly available videos, but even with that,
those videos are long and diverse by nature. Long
videos mean that we must do tons of work to annotate
it thoroughly, and diverse videos suggest that many
classical statistical-based vision algorithms cannot be
Different methods were introduced to approach
surveillance video anomaly detection. Solutions like
(Liu and Ma, 2019) and (Landi et al., 2019) anno-
tated different datasets and addressed the detection
as a fully supervised problem. However, despite the
good results achieved by fully supervised solutions,
they require exhaustive human effort in the data anno-
tation process. Other solutions like (Georgescu et al.,
2021) and (Cai et al., 2021) address the detection as
an unsupervised problem. Although they technically
solved the annotation difficulty, they achieved unap-
pealing results. Also, since most videos on the inter-
net have a title, there is no need to throw away that
information; it is better to use the title as a means to
create a weakly annotated dataset.
The work provided by (Sultani et al., 2018) ad-
dresses those problems by providing a weakly anno-
tated surveillance videos dataset to avoid the hassle
of dataset annotation and builds a Multiple Instance
Learning (MIL) baseline to leverage the weakly an-
notated data. Given a video, we say it is weakly an-
notated when we know the label of this video, but we
do not know the label of each clip (temporal segment)
of the video. For an anomalous video, we know that
the video contains an anomalous clip or more, but we
do not know their exact temporal location. However,
for the normal videos, there exist no anomalous seg-
ments at all; hence, normal videos are fully annotated
by nature.
Despite promising results achieved by the weakly
supervised models, it suffers from a critical problem
due to its learning style. Relying on the Multiple In-
stance Learning and ranking loss optimizes the mod-
els by maximizing the boundary between the anoma-
lous and normal clips. However, maximizing the
El-Tahan, K. and Torki, M.
Semi-supervised Anomaly Detection for Weakly-annotated Videos.
DOI: 10.5220/0010909600003124
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
boundary between two different classes does not guar-
antee that each class will be assigned correctly.
Our Contributions Can Be Summarized as Fol-
We present a novel semi-supervised solution on
top of the weakly supervised model; we produce
pseudo labels from a confident weakly supervised
model and use them to guide the training in as-
signing each clip its correct class.
We investigate different video recognition models
for better video representations.
We achieve results comparable to state-of-the-art
despite using only the superficial (multilayer
perceptron neural network) baseline provided in
(Sultani et al., 2018).
The rest of this paper is structured as follows. Sec-
tion 2 discusses the related work and the different ap-
proaches to resolving the annotation challenge. Sec-
tion 3 presents our methods. Section 4 describes
our experiments and the produced results. Finally,
Section 5 concludes our writing and discusses future
We discuss here the related work for anomaly de-
tection in surveillance videos in the first three sub-
sections. And then, we use the fourth subsection to
discuss semi-supervision related work on image clas-
sification since using semi-supervision in anomaly
detection on weakly-supervised data is a novel ap-
2.1 Fully-supervised
Fully supervised solutions address anomaly detection
in surveillance videos as a labeled classification prob-
lem by completely annotating all the videos. An ex-
ample of those solutions is (Liu and Ma, 2019) which
explores the background bias and then trains a re-
gion loss to drive the network to learn the anomalous
region explicitly. Another example is (Landi et al.,
2019) which studies the impact of considering spa-
tiotemporal tubes instead of whole-frame video seg-
Despite achieving good results, those solutions re-
quire exhausting human resources and high costs for
videos annotation and do not present a practical solu-
tion to utilize all the publicly available videos.
2.2 Unsupervised
Unsupervised anomaly detection in surveillance stud-
ies videos representations to create recognizable pat-
terns without the need for labels at all. For instance,
(Hasan et al., 2016) detect anomalies by creating gen-
erative models to learn the regular motion. Also, (Luo
et al., 2017) proposes a Temporally-coherent Sparse
Coding (TSC) to enforce alike adjacent frames be en-
coded with similar reconstruction coefficients. (Wang
et al., 2018a) suggests a two-stage approach in which
they estimate the normal events globally from the
entire unlabeled videos and then feed the estimated
normal clips into a one-class support vector machine
to build a refined normality model. Other solutions
like (Gong et al., 2019) uses autoencoders to produce
higher reconstruction error for the abnormal inputs
than the normal ones. (Morais et al., 2019) models
the normal patterns of human movements in surveil-
lance video using dynamic skeleton features to iden-
tify human-related anomalous segments. Moreover,
(Park et al., 2020) uses a memory module with a dif-
ferent update design to record the prototypical pat-
terns of normal data. (Wang et al., 2020) suggests
a contrastive representation learning task to establish
subcategories of normality as clusters. (Georgescu
et al., 2021) approaches abnormal event detection
in the video through self-supervised and multi-task
learning at the object level instead of the frame level.
Furthermore, (Cai et al., 2021) uses prior knowledge
of appearance and motion signals to capture their cor-
respondence in the high-level feature space.
Those solutions are creative, and they omit the
need for labels at all. However, their performance
is not appealing compared to other supervised and
weakly supervised solutions. Furthermore, going un-
supervised is a bit extreme since most publicly avail-
able surveillance videos on the internet have titles that
can be utilized as a weak label.
2.3 Weakly-supervised
The UCF-Crime dataset and the baseline provided by
(Sultani et al., 2018) are the origins for all weakly
supervised anomaly detection in surveillance videos.
(Zhu and Newsam, 2019) extends it by proposing an
augmented temporal network to learn a motion-aware
feature. (Zhang et al., 2019) defines an inner bag
loss (IBL) for MIL to constrain the function space
of the weakly supervised problem. Finally, (Feng
et al., 2021) produces a multiple instance self-training
framework (MIST) to refine task-specific discrimina-
tive representations with only video-level annotations.
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
2.4 Semi-supervised
Semi-supervised learning relies on labeled data to
train the model, then uses the model to produce
pseudo labels on the highly certain unlabeled data.
This idea was first introduced on image classification
by (Lee et al., 2013). Later approaches like (Berthelot
et al., 2019b) works by guessing low-entropy labels
for data-augmented unlabeled examples and mixing
labeled and unlabeled data. (Berthelot et al., 2019a)
improves (Berthelot et al., 2019b) by introducing dis-
tribution alignment and augmentation anchoring. Fi-
nally, (Sohn et al., 2020) first generates pseudo-labels
using the model’s predictions on weakly-augmented
unlabeled images and then trained to predict the
pseudo-label when fed a strongly-augmented version
of the same image.
Semi-supervised learning has been used only for
semi-labeled datasets (i.e., part of the dataset is fully
labeled, and the other part is unlabeled). Our work
builds a novel semi-supervision scheme on top of the
weakly supervised baseline (Sultani et al., 2018).
The proposed approach uses a mixup between multi-
ple instance learning (weak supervision) and pseudo
labels (semi-supervision) to optimize the model. The
multiple instance learning is summarized in figure 1.
The pseudo labels training is summarized in figure 2.
The mixup between both approaches is illustrated in
figure 3.
3.1 Multiple Instance Learning
For multiple instance learning we use the baseline
provided by (Sultani et al., 2018), with one difference,
we use SlowFast (Feichtenhofer et al., 2019) instead
of C3D (Tran et al., 2015) for surveillance video fea-
tures extraction.
We start by splitting each surveillance video into
a bag of temporal segments (clips) then apply feature
extraction on those clips. We take pairs of bags (nor-
mal and anomalous) each as one training example.
A ranking loss function would be a straightforward
method to maximize the boundary between the nor-
mal and anomalous bags in each example, as in equa-
tion 1. (Sultani et al., 2018) uses the hinge function
as a ranking loss function.
f (V
) > f (V
) (1)
Where V
and V
represent anomalous and normal
video segments, f (V
) and f (V
) represent the cor-
responding predicted scores produced from the mul-
tilayer perceptron classification neural network, re-
However, in the absence of video segment level
annotations, it is not possible to use equation 1. In-
stead, (Sultani et al., 2018) proposes the following
multiple instance ranking objective function:
f (V
) > max
f (V
) (2)
Where B
and B
represent anomalous bag and
normal bag, and max is taken over all videos segments
in each bag.
The ranking loss in the hinge-loss formulation is
therefore given as follows:
, B
) = max(0, 1 max
f (V
) + max
f (V
)) (3)
However, since anomalous actions occur for a
short time, few segments may contain anomalies,
and since the video is a sequence of segments, the
anomaly score should vary smoothly between adja-
cent video segments. Also, by incorporating sparsity
and smoothness constraints on the instance scores, the
loss function becomes:
, B
) = max(0, 1 max
f (V
) + max
f (V
( f (V
) f (V
+ λ
f (V
Where λ
and λ
represent sparsity and smooth-
ness constraints, respectively.
The final equation after adding the regularization
is as follows:
= L(B
, B
) + kW k
Where MIL stands for multiple instance learning.
3.2 Pseudo Labels
Semi-supervised solutions require semi-labeled
datasets; however, we propose a new approach that
relies on multiple instance learning with weakly
annotated datasets instead of semi-labeled datasets.
We suggest a model similar to FixMatch (Sohn
et al., 2020) in which we train our model on inter-
vals of N epochs where N is a hyperparameter; we
do, however, few modifications to fit our problem.
First, since we do not have fully labeled examples,
we use multiple instance learning instead in the first
few epochs, as shown in figure 3. Once we are more
certain about the model’s results, we start producing
pseudo labels as summarized in figure 2. And finally,
we use those pseudo labels to optimize the classifica-
tion model actively.
Semi-supervised Anomaly Detection for Weakly-annotated Videos
Figure 1: Demonstrates the method of multiple instance learning. We begin feeding the model with pair of videos. Each video
is then divided into a predefined number of temporal segments (clips). Those segments are fed into a SlowFast (Feichtenhofer
et al., 2019) feature extraction model to create two bags of clips features. Finally, we feed those bags into a an MLP classifi-
cation neural network and use a ranking loss to maximize the boundary between the most anomalous video clip and the least
normal (false alarm) video clip.
Figure 2: Illustrates the semi-supervision process. Given an anomalous surveillance video clip, we create two augmented
copies of it. The first copy is weakly augmented by applying KNN background subtraction, and the second is strongly
augmented by using MOG2 background subtraction. We then feed the two clips into the feature extraction module, and
after that, we predict the probabilities of output classes for each clip (normal or anomalous). Suppose the anomalous class
probability in the weak augmentation prediction is above a predefined threshold. In that case, a pseudo label is created and
used to optimize the model with the strong augmentation prediction via binary cross-entropy.
Figure 3: Overall model training, we stack the training into intervals; each interval consists of N epochs where N is a
hyperparameter. In the first interval, we only use multiple instance training until we are more certain about the model’s
prediction; we then increase the number of semi-supervised epochs per interval.
3.2.1 Anomalous Pseudo Labels
For anomalous surveillance video clips (temporal seg-
ments), we produce two copies, one is strongly aug-
mented, and the other is weakly augmented, just like
in FixMatch (Sohn et al., 2020). Unlike in FixMatch,
we do not use RandAugment nor CTAugment for aug-
mentation. According to (Liu and Ma, 2019), surveil-
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
lance anomaly detection models are biased towards
the background, which affects their performance. In-
spired by this observation, we use background sub-
traction algorithms for augmentation. Using back-
ground subtraction meets our requirements to create
augmented versions for the pseudo labels and reduces
the background bias as a plus. We use MOG2 back-
ground subtraction for strong augmentation and KNN
background subtraction for weak augmentation.
Both augmentations (weak and strong) are fed into
the feature subtraction module, and an anomaly score
is predicted for each of them; if the anomaly score for
the weak augmentation is above a predefined thresh-
old, then a pseudo label is produced and is used as
a label to train the model with the strong augmenta-
tion input via binary cross-entropy loss. If otherwise
(the score is below the threshold), no pseudo label is
produced, and the training example is omitted.
3.2.2 Normal Pseudo Labels
Since normal videos are fully annotated (we know the
label of each temporal segment), a pseudo label cre-
ation is not needed because we always have a label.
However, we have to use normal labels with the same
amount as the anomalous pseudo labels to maintain
output classes distribution and prevent output bias.
3.2.3 Mathematical Formulation
The binary cross-entropy loss for normal labels by the
semi-supervision process is defined as follows:
BCE(1, f (V
Where BCE means binary cross-entropy and PL-
Normal means the normal part of the pseudo labels
method, note that while unneeded in equation 6, we
use the concept of bags (i.e., B
) to be able to add this
equation with the multiple instance learning equations
After adding the anomalous pseudo labels loss:
( f
) > th)
(BCE(1, f (V
)) + BCE(0, f
Where PL means pseudo labels, f
means the anomalous output of the strong augmen-
tation input, and f
) means the anomalous
output of the weak augmentation input, and th is a
hyperparameter with a value (0 th 1) that we use
as a predefined threshold.
Note that the condition ( f
) > th) equals
1 if satisfied and 0 otherwise. This condition is multi-
plied to equation 6 too, to prevent classes distribution
From equation 5 and equation 7 the overall loss
function becomes:
L = α L
+ β L
Where α and β are hyperparameters represent-
ing the multiple instance learning percentage and the
pseudo labels training percentage, respectively, and
(α + β = 1).
Finally, hyperparameters th, α, and β are estab-
lished via grid search (LaValle et al., 2004).
In this section we present our experimental setup and
4.1 Experimental Setup
We describe our feature extraction, classification
model and evaluation metrics used.
4.1.1 Feature Extraction
We extract the visual features from the surveillance
videos using the different methods as follows:
We first fix the frame rate to 32 fps. As shown in
SlowFast (Feichtenhofer et al., 2019), having fps
as a power of 2 delivers better time performance
for the feature extraction process.
We then apply the background subtraction if re-
We resize all frames to 240 x 320 for all of them
except for X3D, in which we resize to 312 x 416.
For C3D (Tran et al., 2015), I3D (Carreira and
Zisserman, 2017), I3D NLN (Wang et al., 2018b)
and, SlowFast (Feichtenhofer et al., 2019): We
use the 8x8 R80 architecture.
For X3D (Feichtenhofer, 2020): We use the X3D-
Large architecture.
4.1.2 Classification Model
For the classification model, we use the simple multi-
layer perceptron neural network baseline provided by
(Sultani et al., 2018).
Semi-supervised Anomaly Detection for Weakly-annotated Videos
Table 1: AUC Comparisons with different experiments on top of the (Sultani et al., 2018) baseline. The second column shows
the AUC of the baseline (MIL) with other feature extraction models, third and fourth columns show the AUC of the baseline
with various features extraction models while feeding the input augmented by KNN and MOG2 background subtraction
algorithms, respectively. The fifth column shows the AUC of the semi-supervised on top of weakly annotated data solution
(Multiple Instance Learning + Pseudo Labels) with different feature extraction models.
Feature Extraction Module
C3D (Tran et al., 2015) 75.41 74.20 71.14 78.60
I3D (Carreira and Zisserman, 2017) 76.84 73.45 70.84 79.31
I3D with NLN (Wang et al., 2018b) 77.32 74.01 72.76 79.89
X3D (Feichtenhofer, 2020) 76.98 73.86 72.49 80.06
SlowFast (Feichtenhofer et al., 2019) 79.37 77.70 73.04 81.24
The classification model consists of three fully
connected layers. The first layer has 4096 units, fol-
lowed by 512, 32, then 1 unit. Between every two
layers, we apply 60% dropout for training.
We change, however, the loss sparsity and
smoothness hyperparameters almost per each experi-
ment. We will provide the specific configuration with
the public source code.
4.1.3 Evaluation Metrics
We follow previous (Sultani et al., 2018) in using the
receive operating characteristic (ROC) curve and its
corresponding area under the curve (AUC). However,
we also evaluate our experiments using top accuracy
for only the anomalous class.
4.2 Results and Comparisons
We present different set of experimental results. First,
we show the role of feature extraction and its impact
on the AUC. Next, we show that adding pseudo labels
improves the Multiple Instance Learning framework
as well as anomalous video classification. We also
show the comparative evaluation against state-of-the-
art methods. Finally, we show qualitative results on
the detection we obtain against the baseline of (Sul-
tani et al., 2018).
4.2.1 Effect of Feature Extraction Method
The results shown in table 1 shows superior per-
formance for SlowFast (Feichtenhofer et al., 2019)
as a feature extractor comparing to the other meth-
ods. The 4% enhancement in performance to (Sul-
tani et al., 2018) by using (Feichtenhofer et al., 2019)
alone beats other sophisticated solutions like (Zhu and
Newsam, 2019) and (Zhu and Newsam, 2019) which
suggests that the surveillance anomaly detection mod-
els are struggling with the video representations.
4.2.2 Effect of Background Subtraction and
Pseudo Labeling
In table 1, we show that background subtraction using
KNN or MOG on top of the multiple instance learn-
ing baseline (Sultani et al., 2018) lowered the AUC.
This supports the findings of (Liu and Ma, 2019) that
anomaly detection models on surveillance video are
biased towards the background.
Also, table 1 shows that using the pseudo labels on
top of the multiple instance learning increases consis-
tently the AUC around 2% to 3%, which makes our
solution surpasses the baseline (Sultani et al., 2018)
with around 6% in AUC.
This result confirms our intuition that using
pseudo labels will enhance the performance of the
model of use.
4.2.3 Effect of the Pseudo Labeling on
Anomalous Class Accuracy
The most exciting results are anomalous class accu-
racy shown in table 2. We observe drastic improve-
ment in anomalous class accuracy. This suggests that
maximizing the boundaries between the anomalous
and normal bags via ranking loss and multiple in-
stance learning do not necessarily assign each seg-
ment its correct class. Forcing the model to increase
the separation using the pseudo labels achieved two
goals. The first goal is to increase the separation
between normal and anomalous videos. The second
goal is to assign each video the correct class label.
4.2.4 Comparative Evaluation
We show a comparative evaluation against the state-
of-the-art methods is anomaly detection. Our work
beats most of the state-of-the-art results shown in ta-
ble 3. However, we achieve comparable results to the
best model (Feng et al., 2021). We achieved that with-
out modifying the simple baseline provided by (Sul-
tani et al., 2018). We just use more helpful videos
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
Table 2: Accuracy for the anomalous class at threshold=0.5 for both the weakly supervised solution and the semi-supervised
on top of weak supervision solution; this accuracy is measured for different feature extraction models.
Feature Extraction Module
Anomalous class accuracy
MIL without PL MIL + PL
C3D (Tran et al., 2015) 18.04 42.18
I3D (Carreira and Zisserman, 2017) 21.57 52.07
I3D with NLN (Wang et al., 2018b) 19.71 48.29
X3D (Feichtenhofer, 2020) 24.36 57.42
SlowFast (Feichtenhofer et al., 2019) 23.01 61.78
Figure 4: Qualitative results of our work (blue) method on testing videos compared against (Sultani et al., 2018) (red). The
colored window represents the ground truth anomalous clip. (a), (b), (c), and (d) show videos containing anomalous segments,
while (e) and (f) are normal videos. Our method beats (Sultani et al., 2018) in (a), (b), (c), (d), and (e), however it fails in (f).
representations (SlowFast) and more reliable training
methodology (Pseudo Labels), which suggests future
opportunities for better results.
Table 3: AUC Comparisons between state-of-the-art meth-
ods that rely on weakly supervised data.
Method Reported AUC
(Sultani et al., 2018) 75.41
(Zhu and Newsam, 2019) 79.00
(Zhang et al., 2019) 78.66
(Feng et al., 2021) 82.30
Ours (MIL + PL) 81.24
4.2.5 Qualitative Results
Finally, in figure 4 we show qualitative results of
our work against (Sultani et al., 2018). (a)-(d) show
anomalous videos in which our method beats (Sul-
tani et al., 2018) and produces more accurate results.
Also, in (e), we show that in normal videos, our
method is more robust compared to (Sultani et al.,
2018). We include (f) to represent a few cases in
which our method fails; we believe that in this spe-
cific case, our method was unable to identify that this
is an ordinary visit to a clinic due to background bias
We propose a novel semi-supervision anomaly de-
tection method on surveillance videos. Our novel
method leverages pseudo labels produced by multi-
ple instance learning for weakly supervised datasets.
We also exploit the idea of background bias in
surveillance anomaly detection to build a more robust
pseudo labels augmentation. We use those pseudo
labels for better guidance in the training process
and achieve results comparable to the state-of-the-art
while using a simple multilayer perceptron neural net-
As for future work, we intend to distill the pro-
duced pseudo labels by investigating different ran-
domized augmentations techniques; we also plan to
incorporate the semi-supervision method on the other
state-of-the-art sophisticated classification models to
outperform them and demonstrate the generality of
our approach.
Semi-supervised Anomaly Detection for Weakly-annotated Videos
This work is funded by the Science and Technology
Development Fund STDF (Egypt); Project id: 42519
- Automatic Video Surveillance System for Crowd
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