ANOMALY DETECTION IN INTELLIGENT TRANSPORTATION SYSTEM using real-time video processing and deep learning

ANOMALY DETECTION IN INTELLIGENT
TRANSPORTATION SYSTEM
using real-time video processing and deep learning
Himanshu Moliya
Department of research and development
Planetoid Inc.
Write us for this product: info.planetoid@gmail.com
November, 2022
Himanshu Moliya (Planetoid) Image processing and video processing November, 2022

Summary
1 Motivation and Objectives
2 Introduction
3 Literature Survey
4 Research Gaps
5 Proposed Method
6 Milestone & Gantt Charts
7 Conclusion
8 References

Motivation and Objectives

Motivation
The rise in number of vehicles on the streets, monitoring and safety
become critical part of the intelligent transportation system.
The fact is number of people dying in road accidents every year is
comparatively more than the number of people dying in any war.
Road accidents are happening due to ignorance and avoidance of
traﬀic rules by the people while riding a vehicle on the road.
Road safety campaigns and spreading awareness of following traﬀic
rules can greatly help to reduce road accidents.

Research Objectives
The principal objective of this research is to identify and develop a
solution that deals with any vehicular video and recognizes anomalies
like...
Improper driving,
Illegal road usage (includes travelling in the wrong direction),
Following too closely, Overtaking
Over speeding
Traﬀic lights breaking
In this research, our main focus is on real time video processing and
speed of identification

Introduction
Introduction

Introduction
Introduction
What is anomaly detection?
Anomaly detection in intelligent transportation system (ITS) is the
identification of rare items, events, or observation that deviates
significantly from the majority of the vehicles.

Introduction
Domain introduction
Image and video processing
Our primary focus is on Real time video processing
Speed (faster processing)
Identification anomalies in any street video (without predefined
dataset and model training) - Universal solution
Multiple cameras (at same time)
Store insights only not huge sizes videos
Sub domains
Computer vision
Deep learning
AI and machine learning

Introduction
Applications
Real life use cases
Vehicle surveillance system
Traﬀic Analytics
E-challan generation
Automatic driving permit test systems
Track vehicle activities inside private organization

Literature Survey
Literature Survey

Literature Survey
Literature Survey - Road Accident Scenario
Main contributing factors leading to road accidents are:
Human Factor
Driver - Improper driving
Driver - Speeding
Driver - Inattention/mis-judgement
Pedestrian - risky behaviour and inattention
Road Infrastructure Factor
Lack of proper road marking and signage
Lack of proper pedestrian infrastructure
Deficiencies in road infrastructure
Lack of road maintenance
Vehicle Factor
Vision obstruction
Defective tires
Over-loading of people
Absence of reflectors
Source: [2] [4] [5] [6]

Literature Survey
Literature Survey - Road Accident Scenario
Figure 1 : DISTRIBUTION OF 156 ACCIDENTS BY CONTRIBUTING FACTORS IN
AHMEDABAD REGION
Source: [2]

Literature Survey
Literature Survey - Main contributing factors
Driver - Improper driving (constitutes 58% accidents within Human
Factor)
Improper lane change/lane usage
Illegal road usage (includes traveling in the wrong direction)
Sudden steering / braking / both; Following too closely
Overtaking
42% of total accidents are due to improper road infrastructure
Lack of proper marking
Deficiencies in intersection design
Undivided road
Missing or improper pedestrian crossings
Vision obstruction due to trees / plantation / poles / road objects
Defective road surface
Source: [2] [4] [5] [6]

Literature Survey
Literature Survey - Pipeline for anomaly detection
Input video -> Object detection -> trajectory Extraction -> Anomaly
Identification
Deep learning used for Object detection task
Real time multi processing used for Anomaly Identification
In this research,
Improve Anomaly Identification with more use cases and high speed
Create fastest object detection using deep learning technique
Source: [1]

Literature Survey
Literature Survey - Object detection speed comparison
Figure 2 : Object detection speed comparison
Source: [7]

Literature Survey
Literature Survey - Object detection methods accuracy
Figure 3 : Object detection methods
Source: [7]

Research Gaps
Research Gaps

Research Gaps
Research Gaps
No eﬀicient system available which can identify these anomalies (In
Indian context)
Improper driving
Improper lane change/lane usage
Illegal road usage (includes travelling in the wrong direction)
Sudden steering / braking / both; Following too closely
Overtaking
Overspeeding
Traﬀic lights breaking

Research Gaps
Research Gap
Contact us for more info about this product:
info.planetoid@gmail.com

Proposed Method
Proposed Method

Proposed Method
Proposed method
Architectural model which can provide solution of research gaps
Which can identify anomalies in real time
Identification should be automatic in nature
Solution should work with any camera

Proposed Method
Proposed method
Contact us for more info about this product:
info.planetoid@gmail.com

Milestone & Gantt Charts

Milestone - Review process
Steps to follow for research project:
✓Completion of idea phase
✓Concept approval
✓Requirements review
• Preliminary design review
• Critical design review
• Test plan review
• System test review

Gantt Charts
Figure 5 : Research plan - Gantt Chart 2022

Gantt Charts
Figure 6 : Research plan - Gantt Chart 2023

Conclusion
Conclusion

Conclusion
Conclusion
At end of literature review, We have successfully able to identify
research gaps and also proposed architecture for an anomaly
detection.
In a next step, Will look into Research methodology planning and
implementation

References
References

References
References
[1] Planetoid INC. Contact us: info.planetoid@gmail.com
[2] Ahmedabad Urban Road Accident Study 2016 conducted by JP RESEARCH INDIA PVT LTD,for COMMISSIONER OF
TRANSPORT, GOVT. OF GUJARAT, India.
[4] Tan, H., Zhai, Y., Liu, Y. and Zhang, M., 2016, March. Fast anomaly detection in traffic surveillance video based on robust
sparse optical flow. In 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp.
1976-1980). IEEE.
[5] Rahman, Z., Ami, A.M. and Ullah, M.A., 2020, June. A real-time wrong-way vehicle detection based on YOLO and
centroid tracking. In 2020 IEEE Region 10 Symposium (TENSYMP) (pp. 916-920). IEEE.
[6] Şentaş, A., Kul, S. and Sayar, A., 2019, September. Real-time traffic rules infringing determination over the video stream:
wrong way and clearway violation detection. In 2019 International Artificial Intelligence and Data Processing Symposium
(IDAP) (pp. 1-4). IEEE.
[8] Farooq, M.U., Khan, N.A. and Ali, M.S., 2017. Unsupervised video surveillance for anomaly detection of street traffic.
International Journal of Advanced Computer Science and Applications, 8(12).
[9] Kaltsa, V., Briassouli, A., Kompatsiaris, I. and Strintzis, M.G., 2018. Multiple Hierarchical Dirichlet Processes for anomaly
detection in traffic. Computer Vision and Image Understanding, 169, pp.28-39.
[10] Wang, S., Wang, P., Wang, J. and Jin, Y., 2020, November. Vehicle trajectory recognition based on video object
detection. In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1679-1683). IEEE.
[11] Sabour, S., Rao, S. and Ghaderi, M., 2021, September. Deepflow: Abnormal traffic flow detection using Siamese networks.
In 2021 IEEE International Smart Cities Conference (ISC2) (pp. 1-7). IEEE.

Thank you!

ANOMALY DETECTION IN INTELLIGENT TRANSPORTATION SYSTEM using real-time video processing and deep learning

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