Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking Article Swipe
Steven Carrell
,
Amir Atapour–Abarghouei
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.02119
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.02119
The use of mobiles phones when driving have been a major factor when it comes to road traffic incidents and the process of capturing such violations can be a laborious task. Advancements in both modern object detection frameworks and high-performance hardware has paved the way for a more automated approach when it comes to video surveillance. In this work, we propose a custom-trained state-of-the-art object detector to work with roadside cameras to capture driver phone usage without the need for human intervention. The proposed approach also addresses the issues caused by windscreen glare and introduces the steps required to remedy this. Twelve pre-trained models are fine-tuned with our custom dataset using four popular object detection methods: YOLO, SSD, Faster R-CNN, and CenterNet. Out of all the object detectors tested, the YOLO yields the highest accuracy levels of up to 96% (AP10) and frame rates of up to ~30 FPS. DeepSort object tracking algorithm is also integrated into the best-performing model to collect records of only the unique violations, and enable the proposed approach to count the number of vehicles. The proposed automated system will collect the output images of the identified violations, timestamps of each violation, and total vehicle count. Data can be accessed via a purpose-built user interface.
Related Topics
Concepts
Computer science
Timestamp
Identification (biology)
Phone
Object (grammar)
Object detection
Task (project management)
Video tracking
Process (computing)
Frame (networking)
Interface (matter)
Detector
Computer vision
Artificial intelligence
Real-time computing
Frame rate
Tracking (education)
State (computer science)
Pattern recognition (psychology)
Engineering
Telecommunications
Pedagogy
Botany
Linguistics
Operating system
Biology
Philosophy
Psychology
Bubble
Parallel computing
Algorithm
Maximum bubble pressure method
Systems engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2109.02119
- https://arxiv.org/pdf/2109.02119
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286985960
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4286985960Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2109.02119Digital Object Identifier
- Title
-
Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with TrackingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-05Full publication date if available
- Authors
-
Steven Carrell, Amir Atapour–AbarghoueiList of authors in order
- Landing page
-
https://arxiv.org/abs/2109.02119Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2109.02119Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2109.02119Direct OA link when available
- Concepts
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Computer science, Timestamp, Identification (biology), Phone, Object (grammar), Object detection, Task (project management), Video tracking, Process (computing), Frame (networking), Interface (matter), Detector, Computer vision, Artificial intelligence, Real-time computing, Frame rate, Tracking (education), State (computer science), Pattern recognition (psychology), Engineering, Telecommunications, Pedagogy, Botany, Linguistics, Operating system, Biology, Philosophy, Psychology, Bubble, Parallel computing, Algorithm, Maximum bubble pressure method, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.display_name | arXiv (Cornell University) |
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| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2109.02119 |
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| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2109.02119 |
| publication_date | 2021-09-05 |
| publication_year | 2021 |
| referenced_works_count | 0 |
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