PowerPoint Presentation

IMAGE PROCESSING BASED TRAFFIC VIOLATION DETECTION AND REPORTING SYSTEM.

[Audio] Our main objective in this project is to detect and automatically report traffic rule violations. We use dashcam video footage and apply AI-based image processing techniques to identify traffic violations, such as riding without a helmet, triple riding, or jumping signals. Once detected, the system will automatically report these violations to authorities through social media platforms like Twitter. This eliminates the need for manual monitoring and makes reporting faster and more reliable.".

[Audio] Road accidents in India claim more than 1.5 lakh lives every year. A large number of these accidents are caused by common traffic violations such as signal jumping, wrong-way driving, and helmetless riding. At present, traffic enforcement is largely manual, which comes with challenges like human error, corruption, and lack of adequate surveillance infrastructure. These limitations make it difficult to ensure consistent and fair monitoring of violations. Therefore, there is a clear need for automated and real-time systems that can support traffic departments. Such systems can continuously monitor violations, reduce dependency on manual enforcement, and help improve overall road safety. The images here highlight the problem—limited surveillance cameras, manual checks by officers, and crowded roads where violations often go unnoticed. This is the motivation behind developing our AI-powered automated traffic violation detection and reporting system..

[Audio] This slide presents statistical evidence that highlights the seriousness of traffic violations. Firstly, signal jumping alone accounts for nearly 7% of all road accidents in the city every year, showing how a single violation can contribute significantly to accidents. Secondly, helmet compliance has improved. Violations fell by about 44% between 2022 and 2023, largely due to the use of modern enforcement tools such as ANPR cameras and AI-based monitoring systems. This shows that technology-driven approaches can make a real difference. Thirdly, when we look at accident trends, the total number of accidents rose slightly from 3,452 in 2022 to 3,642 in 2023. However, the number of fatal accidents remained almost constant at around 500, with 504 fatalities recorded in 2023. The charts here provide further insight: the bar graph shows accident counts across major cities, while the pie chart breaks down the distribution of violations. As we can see, helmetless driving and wrong-way driving together form the majority of violations, followed by signal jumping and overspeeding. These statistics reinforce the need for automated, AI-based systems to continuously monitor and reduce such violations.".

[Audio] In this slide, we define the core problem that our project is addressing. Traffic violations such as signal jumping, helmetless riding, overspeeding, and wrong-way driving directly lead to accidents and road congestion. These violations are everyday issues but have a huge impact on road safety. Currently, there are two main challenges: First, manual monitoring by traffic police is labor-intensive and prone to human errors. It is nearly impossible for officers to continuously monitor every violation on busy roads. Second, most existing surveillance systems are limited to basic recording. They lack real-time detection capabilities and cannot automatically generate reports or alerts for authorities. The images here illustrate the problem — from helmetless and triple riding to wrong-way driving, these violations are common but not always detected. This makes it clear that we need a smarter, automated solution for traffic enforcement..

[Audio] Our proposed solution is an AI-powered traffic violation detection and reporting system. Currently, surveillance is limited to fixed cameras at traffic junctions and signals. These systems can only record footage and do not support real-time automated detection. Our system addresses this gap by using dashcam inputs to process live video with computer vision techniques. The dashcam footage is analyzed in real time to detect violations such as helmetless riding, triple riding, overspeeding, and wrong-way driving. Once a violation is detected, the vehicle's license plate is identified using Optical Character Recognition. To improve detection accuracy, we use advanced computer vision algorithms and deep learning models like YOLO. The system then automatically generates a report containing the vehicle number, timestamp, and location. This information is directly sent to government authorities through their official Twitter account, ensuring quick and transparent reporting. The expected outcomes are enhanced accuracy in violation detection, reduced human intervention, and faster enforcement. Overall, this improves road safety and helps traffic departments manage violations more efficiently.".

[Audio] Our methodology consists of five major stages, starting from video acquisition to automatic reporting. Step 1 – Data Acquisition: We capture real-time video feed from a dashcam and simultaneously fetch the vehicle's GPS location, including latitude and longitude. This GPS data is attached to each video frame for accurate location tracking of violations. Step 2 – Pre-processing of Video Frames: Before analysis, the frames are enhanced through noise reduction, brightness and contrast adjustment, and region-of-interest selection. This ensures better clarity for detection models. Step 3 – Traffic Violation Detection: Using computer vision and deep learning models such as YOLO or SSD, we detect violations like signal jumping, overspeeding, wrong-way driving, or helmetless riding. The system marks the specific frames where violations occur. Step 4 – Number Plate Detection and Recognition: Once a violation is flagged, the vehicle's number plate is detected using edge detection and bounding box methods. The alphanumeric license number is then extracted using OCR tools like Tesseract or EasyOCR. Step 5 – Report Generation: A structured violation report is generated. It includes the vehicle number, violation type, GPS location with a Google Maps link, date, time, and a snapshot image of the violation. Step 6 – Automatic Reporting via Twitter: Finally, the system posts the violation details to the traffic police department's official Twitter account using the Twitter API. The post includes the violation message, snapshot, and precise location details, enabling quick and transparent action by authorities. This pipeline ensures end-to-end automation—from detection to reporting—without requiring manual intervention.".

[Audio] Our system requires both hardware and software components to function effectively. Hardware Components: We use a dashcam mounted on the vehicle dashboard to continuously capture real-time road video. The power source can be either the car battery or a dedicated power bank to ensure uninterrupted operation. For storage, we use an SD card or onboard memory to temporarily store the captured video data before processing. Software and Tools: For video processing, we use OpenCV in Python to handle frame extraction and preprocessing. For object detection, we implement the YOLOv8 model, which is well-suited for real-time detection tasks. Number plate recognition is performed using OCR tools like EasyOCR or Tesseract OCR. Violation detection logic is handled through custom Python scripts that integrate these modules. Finally, violation reporting is automated using the Twitter API, which enables direct communication with traffic authorities through their official accounts. In short, our system combines affordable hardware with powerful AI-based software to create a fully automated violation detection and reporting pipeline.".

[Audio] This slide explains the complete workflow of our system, step by step. Step 1 – Dashcam Video Capture: The dashcam, mounted on the vehicle dashboard, records real-time traffic footage as the car drives on public roads. Step 2 – Video Frame Extraction: The recorded video is divided into individual frames using OpenCV. These frames are then used for real-time analysis. Step 3 – Traffic Violation Detection: Each frame is analyzed using the YOLOv8 model to detect violations such as helmetless riding, wrong-way driving, triple riding, and signal jumping. The system marks the specific frames where a violation occurs. Step 4 – Number Plate Recognition: Once a violation is detected, the vehicle's number plate is zoomed in and extracted. Using OCR tools like EasyOCR or Tesseract, the alphanumeric number is recognized and stored as text. Step 5 – Auto-Reporting to Twitter: Finally, an automated script generates a violation message containing the vehicle number, violation type, time, and location. This message, along with an image of the violation, is posted directly to the official traffic police or government Twitter account using the Twitter API. The small diagrams on the right visually summarize this pipeline—from capturing video, detecting violations, recognizing number plates, and finally posting the violation details on social media for enforcement.".

[Audio] With this, we conclude our presentation. Our project demonstrates how AI and image processing can be used to automatically detect traffic violations and report them directly to authorities, reducing human error and improving road safety. We believe this solution can support traffic departments in achieving faster, fairer, and more efficient enforcement..