University of Computer Studies, Mandalay
Abnormal Driving Behavior
Detection for Driver Assistance
System Using YOLO
Title Presentation
Khaing Zar Myint Aung
(MKPT-3773)
Date: 9.5.2023
Outline
•
•
•
•
•
•
Abstract
Introduction
State of Research
Aim and Objectives
Motivation
Contribution
• Overview of the Proposed
System
• Background Theory
• Work Plan
• Conclusion
• References
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Abstract
•
•
•
•
•
The ability to robustly detect abnormal driving behavior has
the potential to limit traffic accidents and save many lives.
Abnormal driving behavior that threatens road safety includes
aggressive, anxious, nervous, and unstable driving.
Any of these can lead to dangerous situations in traffic.
Therefore, the system aims to provide a robust mechanism to
detect such abnormal driving behavior.
To handle the abnormal driving problem, driver assistance
system have been developed.
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Abstract (Cont’d)
•
•
Abnormal driving behavior detection is based on the
detection of phone, face movements, smoking conditions,
drinking conditions while driving and drowsiness.
The system proposes YOLO based abnormal behavior
detection algorithm to help the driving system and to reduce
traffic accidents.
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Introduction
• Traffic safety remains one of the main areas of study in
vehicular technology.
• Road safety continues to be a major concern in both industry
and academia.
• Nowadays, vehicles offer several safety and convenience
features including partially or even fully autonomous driving.
• Despite of that, the prevalent reason for accidents remains to
be human reaction and abnormal driving behaviour.
• The first step in mitigating such behaviour is to detect it in
order to increase safety.
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Introduction (Cont’d)
• Abnormal driving is mostly caused by drunkenness,
recklessness, and fatigue.
• With the rising popularity of smart phones, mobile use is now
the leading cause of death behind the wheel.
• Normal driving is characterized by good speed control of the
vehicle and the avoidance of sudden acceleration.
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State of Research
No
Authors
1.
Liu, C., Tao,
Y., Liang, J.,
Li, K., &
Chen, Y.
In 2018 IEEE
4th
information
technology
and
mechatronics
engineering
conference
(ITOEC) (pp.
799-803).
Title
Object
detection
based on
YOLO
network
Method Summary
Application Area
Issues
YOLO neural network
used to analyze for
research object.
The experiment was based
on the Darknet-53 network
structure.
Traffic signs dataset
Training images:
1318
Testing images: 334
Total: 1652 images
The trained model
has better
generalizing ability
and higher
robustness only
with the degraded
training sets.
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State of Research (Cont’d)
No
Authors
2.
Lin, J.
In 2020
IEEE
Internatio
nal
Conferen
ce on
Power,
Intelligent
Computin
g and
Systems
(ICPICS)
(pp. 420424).
Title
Integrated
Intelligent
Drowsiness
Detection
System
Based on
Deep
Learning
Method Summary
Integrated Drowsiness
Detection Systemcombined the heart rate
monitoring system and
the facial expression
detection system using
Resnet50 for
classification
Application Area
Self-collected dataset for
drowsiness detection and
precaution system
University of Computer Studies, Mandalay
Issues
The authors don’t
state that the
integrated
drowsiness detection
system is better in
time efficiency than
other methods.
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State of Research (Cont’d)
No
Authors
Title
Method Summary
3.
Zhao, L., &
Wan, Y.
(2019,
December).
In 2019
IEEE 5th
Internation
al
Conference
on
Computer
and
Communic
ations
(ICCC) (pp
. 21182122).
IEEE.
A New
Deep
Learning
Architectu
re for
Person
Detection
Person Detection network
model (PDnet) –improved
the Yolo3 network model
and the clustering function
Application Area
PASCALVOC dataset
Training data: selected 5k photos
Testing data: selected 3k photos
University of Computer Studies, Mandalay
Issues
In complex
scenarios, the
algorithm
can’t detect
the object
completely but
still provides
the possibility
of low iou
value.
9
Aim and Objectives
Aim
• The aim of the research is to develop detection algorithm
that can be used in detecting abnormal driving behavior
recognition system in real-time.
Objectives
• To assist the driving system and to reduce traffic accidents
• To detect drowsiness and abnormal driving behavior
• To become faster and more accurate in detecting objects
• To evaluate the performance of the proposed system
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Motivation
• According to the latest WHO data published in 2020 Road
Traffic Accidents Deaths in Myanmar reached 11,004 or
3.05% of total deaths.
• The age adjusted Death Rate is 20.94 per 100,000 of
population ranks Myanmar #71 in the world.
• Deaths and injuries related to traffic accidents remain a major
concern in Myanmar, which were mostly caused by human
errors such as over speeding, negligent driving, drowsy driving
and others.
• One solution is to help driver to develop driver assistance
system.
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Contribution
• The main contribution of the research is to construct the
detection network architecture by using YOLO algorithm for
abnormal driving behavior detection.
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Overview of the Proposed System
Training Process
Testing Process
Input Video or Image
Input Video
Labelling for Training
Behavior Detection
Image Preprocessing
Results and Post Processing
Proposed Network Model
Trained
Network
Model
Figure 1. Overview of the Proposed System
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Procedures of the Proposed System
• The procedures of the proposed system consist of the training
process and testing process.
• In the training process, firstly the user takes the video or image as
input.
• Then, the system makes a ground truth labelling by using the
image labeller function in MATLAB for training.
• In the stage of pre-processing, data augmentation is applied to the
label image.
• After pre-processing, the system extracts the features of image by
using the proposed network model and train the image dataset.
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Procedures of the Proposed System
(Cont’d)
• In the testing process, the input is obtained from the video or
webcam.
• The proposed system is captured these input frame and make
behavior detection by using proposed detection model.
• Then, the system will produce the results, make performance
analysis and compare with traditional methods.
• This research experiments on the datasets of primary sources or
self-collected dataset.
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Background Theory
• Object detection is a phenomenon in computer vision that
involves the detection of various objects in digital images or
videos.
• Some of the objects detected include people, cars, chairs,
stones, buildings, and animals.
• Object detection can be roughly divided into region proposals
object detection algorithms such as RCNN, FAST-RCNN,
FASTER-RCNN, and regression object detection algorithms
such as SSD and YOLO.
• Object detection network can be categorized into two main
types: a two-stage network or a single-stage network.
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Background Theory (Cont’d)
Two-Stage Networks
• The initial stage of two-stage networks, such as R-CNN and its
variants, identifies region proposals, or subsets of the image
that might contain an object.
• The second stage classifies the objects within the region
proposals.
• Two-stage networks can achieve very accurate object detection
results; however, they are typically slower than single-stage
networks.
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Background Theory (Cont’d)
Figure 2. Overview of High-level architecture of R-CNN and Fast R-CNN object
detection
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Background Theory (Cont’d)
Single-Stage Networks
• In single-stage networks, such as YOLO v2, the CNN
produces network predictions for regions across the entire
image using anchor boxes, and the predictions are decoded to
generate the final bounding boxes for the objects.
• Single-stage networks can be much faster than two-stage
networks, but they may not reach the same level of accuracy,
especially for scenes containing small objects.
Figure 3. Overview of YOLO v2 object detection
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Background Theory (Cont’d)
• From the point of view of computer vision, the abnormal
driving behavior detection system is proposed by the
traditional computing method and deep learning method.
• Traditional computing methods take a long processing time for
detection in real-time and the accuracy is lower than the deep
learning method.
• Although Convolutional Neural Network (CNN) based
detection has high detection accuracy and faster computing
time, it also takes a little bit long processing time per frame.
• Although these approaches have solved the challenges of data
limitation and modeling in object detection, they are not able
to detect objects in a single algorithm run.
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Background Theory (Cont’d)
• YOLO (You Only Look Once) algorithm employs
convolutional neural networks (CNN) to detect objects in realtime.
• As the name suggests, the algorithm requires only a single
forward propagation through a neural network to detect
objects.
• This means that prediction in the entire image is done in a
single algorithm run.
• The CNN is used to predict various class probabilities and
bounding boxes simultaneously.
• The YOLO algorithm consists of various variants.
• Some of the common ones include tiny YOLO and YOLOv3.
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Background Theory (Cont’d)
• YOLO algorithm is important because of the following
reasons:
• Speed: This algorithm improves the speed of detection because
it can predict objects in real-time.
• High accuracy: YOLO is a predictive technique that provides
accurate results with minimal background errors.
• Learning capabilities: The algorithm has excellent learning
capabilities that enable it to learn the representations of objects
and apply them in object detection.
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Background Theory (Cont’d)
• An abnormal driving behavior detection system must alert the
driver to prevent accidents in the driving state where the
vehicle is moving at high speed.
• So the detection algorithm must have high detection accuracy
and real-time processing.
• YOLO is a popular object detection algorithm that has
revolutionized the field of computer vision.
• It is fast and efficient, making it an excellent choice for realtime object detection tasks.
• This algorithm is popular because of its speed and accuracy.
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Work Plan
Jan to Apr (2023)
Conduct continuous, through literature review to identify gaps in existing
algorithms and experts in the field
Finding datasets for application area
Prepare for seminar
May to Oct (2023)
Identify proposed algorithm based on research vision, plan, experimental
results and literature review results.
Prepare for seminar
Nov to May (2024) Data preprocessing and testing with dataset and proposed algorithm
Prepare for program
Prepare for paper and then edit
Prepare for seminar
Jun to Dec (2024)
Rewrite and rewrite a paper based on reviewer comments
Prepare for seminar
Implement the program
Jan to June (2025)
Implement the program
Prepare for seminar
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Conclusion
• This research proposed abnormal driving behaviour detection
by using the proposed detection model which is based on
YOLO.
• The system is developed a well object detection algorithm for
detecting drowsiness and abnormal driving behaviour
conditions.
• In real world application, abnormal driving behaviour
detection system can cooperate with alarm system and other.
• The system can help prevent road accidents on a large scale
and prevent loss of life as well as vehicle damage.
• Car manufacturers can also think about adding the driver
assistance system as a feature in the infotainment systems of
their cars.
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References
[1] Liu, C., Tao, Y., Liang, J., Li, K., & Chen, Y. (2018, December).
Object detection based on YOLO network. In 2018 IEEE 4th
information technology and mechatronics engineering conference
(ITOEC) (pp. 799-803).
[2] Lin, J. (2020, July). Integrated Intelligent Drowsiness Detection
System Based on Deep Learning. In 2020 IEEE International
Conference on Power, Intelligent Computing and Systems
(ICPICS) (pp. 420-424). IEEE.
[3] Zhao, L., & Wan, Y. (2019, December). A New Deep Learning
Architecture for Person Detection. In 2019 IEEE 5th International
Conference on Computer and Communications (ICCC) (pp. 21182122). IEEE.
[4] Hu, J., Zhang, X., & Maybank, S. (2020). Abnormal driving
detection with normalized driving behavior data: a deep learning
approach. IEEE transactions on vehicular technology, 69(7), 69436951.
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Thank You