This project implements a real-time computer vision system to detect and track vehicles violating emergency lane restrictions using YOLOv12 object detection technology. The system monitors a user-defined region of interest (ROI) and counts vehicles that cross the designated safety lane boundary. Designed for traffic management and enforcement applications, the system combines object detection, multi-object tracking, and line-crossing detection capabilities to provide comprehensive violation monitoring with live dashboard analytics.
The system employs a multi-stage pipeline combining object detection, tracking, and spatial analysis:
Object Detection: YOLOv12 model detects vehicles (cars, motorcycles, buses, trucks) in each frame with high accuracy and speed.
Multi-Object Tracking: Objects are tracked across frames using unique IDs, maintaining consistency in vehicle identification and enabling trajectory analysis.
ROI Filtering: Only vehicles with center points within the user-defined region of interest are processed, reducing false alerts and focusing on relevant areas.
Line Crossing Detection: The system calculates if a vehicle trajectory intersects the safety lane boundary by analyzing the cross product of path vectors, determining violation direction.
Dashboard Analytics: Real-time statistics display total vehicles, violation counts, violation ratios, and breakdowns by vehicle type.
[Video Input]
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[YOLOv12 Detection] โ [Multi-Object Tracking]
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[ROI Filtering]
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[Line Crossing Detection] โ [Violation Classification]
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[Dashboard & Visualization]
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[Video Output]The system uses the cross product of 2D vectors to determine if a vehicle has crossed the safety line and in which direction.
Vector Definitions:
Line vector (from point A to point B on the safety line):
$$\mathbf{v}_{\text{line}} = (B_x - A_x, B_y - A_y)$$
Path vector (from previous position to current position):
$$\mathbf{v}_{\text{path}} = (P_{\text{current},x} - P_{\text{previous},x}, P_{\text{current},y} - P_{\text{previous},y})$$
2D Cross Product (Scalar Result):
$$\mathbf{v}_1 \times \mathbf{v}_2 = x_1 y_2 - x_2 y_1$$
For line crossing detection:
$$C = \mathbf{v}_{\text{line}} \times \mathbf{v}_{\text{path}} = (B_x - A_x)(P_{\text{current},y} - P_{\text{previous},y}) - (B_y - A_y)(P_{\text{current},x} - P_{\text{previous},x})$$
Crossing Direction Determination:
$$\text{Direction} = \begin{cases} \text{Left to Right} & \text{if } C > 0 \\ \text{Right to Left} & \text{if } C < 0 \\ \text{No Crossing} & \text{if } C = 0 \end{cases}$$
Violation Condition:
A violation is detected when:
$$|C| > \epsilon \quad \text{and} \quad \text{sign}(C_{\text{prev}}) \neq \text{sign}(C_{\text{current}})$$
where $\epsilon$ is a small threshold to filter numerical noise.
To determine if a vehicle center point $P = (x, y)$ is inside the ROI polygon with vertices $V = \{V_1, V_2, ..., V_n\}$, the system uses the ray-casting algorithm:
$$\text{Inside}(P) = \sum_{i=1}^{n} \mathbb{1}_{\text{intersect}}(P, V_i, V_{i+1}) \mod 2$$
where:
Intersection Test:
For edge from $V_i = (x_i, y_i)$ to $V_{i+1} = (x_{i+1}, y_{i+1})$:
$$\text{Intersects} = \begin{cases} \text{True} & \text{if } (y_i > y) \neq (y_{i+1} > y) \text{ and } \\ & x < \frac{(x_{i+1} - x_i)(y - y_i)}{(y_{i+1} - y_i)} + x_i \\ \text{False} & \text{otherwise} \end{cases}$$
For a bounding box with coordinates $(x_1, y_1, x_2, y_2)$:
$$P_{\text{center}} = \left(\frac{x_1 + x_2}{2}, \frac{y_1 + y_2}{2}\right)$$
where:
Total Violation Count:
$$N_{\text{violations}} = \sum_{c \in \text{Classes}} N_c$$
where $N_c$ = number of violations for vehicle class $c$ (car, motorcycle, bus, truck).
Violation Ratio:
$$R_{\text{violation}} = \frac{N_{\text{violations}}}{N_{\text{total}}} \times 100\%$$
where $N_{\text{total}}$ = total number of tracked vehicles.
Per-Class Violation Rate:
$$R_c = \frac{N_c}{N_{\text{violations}}} \times 100\%$$
The system maintains a trajectory history buffer for each tracked vehicle:
$$\mathcal{H}_{\text{ID}} = \{P_1, P_2, ..., P_k\}$$
where:
Path Segment:
$$\mathbf{S}_i = P_{i+1} - P_i = (x_{i+1} - x_i, y_{i+1} - y_i)$$
Trajectory Length (Total Distance Traveled):
$$D_{\text{total}} = \sum_{i=1}^{k-1} |\mathbf{S}_i| = \sum_{i=1}^{k-1} \sqrt{(x_{i+1} - x_i)^2 + (y_{i+1} - y_i)^2}$$
Bounding Box Color:
$$\text{Color}_{\text{box}} = \begin{cases} (255, 0, 0) & \text{if vehicle is violator (Red)} \\ (0, 0, 255) & \text{if vehicle is normal (Blue)} \end{cases}$$
Transparency (Alpha Channel):
$$\alpha = \begin{cases} 0.7 & \text{if violator (70\% opaque red fill)} \\ 0.0 & \text{if normal (no fill, only border)} \end{cases}$$
Dashboard Metrics Display:
$$\text{Dashboard} = \begin{bmatrix} \text{Total Vehicles:} & N_{\text{total}} \\ \text{Violations:} & N_{\text{violations}} \\ \text{Violation Rate:} & R_{\text{violation}} \\ \text{Cars:} & N_{\text{car}} \\ \text{Motorcycles:} & N_{\text{motorcycle}} \\ \text{Buses:} & N_{\text{bus}} \\ \text{Trucks:} & N_{\text{truck}} \end{bmatrix}$$
python>=3.7
opencv-python>=4.5.0
numpy>=1.21.0
ultralytics>=8.0.0# Clone the repository
git clone https://github.com/kemalkilicaslan/Safety-Lane-Violation-Detection-System.git
cd Safety-Lane-Violation-Detection-System
# Install required packages
pip install -r requirements.txtSafety-Lane-Violation-Detection-System/
โโโ Safety-Lane-Violation-Detection-System.py
โโโ README.md
โโโ requirements.txt
โโโ LICENSEyolo12x.pt (automatically downloaded on first run)python Safety-Lane-Violation-Detection-System.pyUpdate these parameters in the script:
# Video input/output
video_capture = cv2.VideoCapture("Traffic-Flow.mp4") # Input video file
output_file = 'Safety-Lane-Violation-Detection.mp4' # Output video file
# ROI coordinates (define as quadrilateral points)
ROI_COORDINATES = np.array([[770, 280], [1130, 280], [1500, 900], [100, 900]], dtype=np.int32)
# Counting line coordinates
LINE_COORDINATES = np.array([(375, 650), (615, 650)], dtype=np.int32)q to quit the applicationThe processed video is saved as:
Safety-Lane-Violation-Detection.mp4Traffic Flow:
Safety Lane Violation Detection:
| Class ID | Vehicle Type |
|---|---|
| 2 | Car |
| 3 | Motorcycle |
| 5 | Bus |
| 7 | Truck |
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โ YOLOv12 DETECTION WITH TRACKING โ
โ Output: Bounding boxes with class IDs and tracking IDs โ
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โ VEHICLE FILTERING โ ROI CENTER POINT CHECK โ
โ (Classes 2,3,5,7) โ (Is center inside polygon?) โ
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โ TRAJECTORY TRACKING AND HISTORY UPDATE โ
โ Maintain 30-frame history of center points per vehicle โ
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โ LINE CROSSING DETECTION ALGORITHM โ
โ โข Extract previous and current center positions โ
โ โข Calculate line-path intersection โ
โ โข Compute cross product for direction determination โ
โ โข Mark vehicle as violator if line crossed โ
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โ COUNT VIOLATIONS โ UPDATE STATISTICS โ
โ (By vehicle type) โ (Totals and percentages) โ
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โ VISUALIZATION AND DASHBOARD RENDERINGโ
โ โข Annotate frame with bounding boxes โ
โ โข Display live statistics panel โ
โ โข Show pie chart of violations โ
โ โข Render vehicle type breakdown โ
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โ Output Frame โ
โโโโโโโโโโโโโโโโThe system uses vector geometry to detect violations:
| Library | Version | Purpose |
|---|---|---|
| opencv-python | 4.5+ | Video I/O, image processing, visualization |
| ultralytics | 8.0+ | YOLOv12 model and tracking inference |
| numpy | 1.21+ | Array operations and vector mathematics |
YOLOv12 (Extra Large): yolo12x.pt
This project is open source and available under the Apache License 2.0.
Special thanks to the Ultralytics team for developing and maintaining the YOLO framework and YOLOv12 models. This project benefits from the OpenCV community's excellent computer vision tools and documentation. Sample traffic footage used for demonstration purposes only.
Note: Ensure compliance with local traffic laws and privacy regulations when deploying vehicle monitoring systems. This system is intended for authorized traffic management, law enforcement, and research purposes only. Always verify the legal implications of recording vehicle data in your jurisdiction.