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Add speed_estimation and distance_calculation in ultralytics solutions (#7325)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-01-05 14:38:13 +05:00 • committed by GitHub
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---
comments: true
description: Speed Estimation Using Ultralytics YOLOv8
keywords: Ultralytics, YOLOv8, Object Detection, Speed Estimation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
---
# Speed Estimation using Ultralytics YOLOv8 🚀
## What is Speed Estimation?
Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](https://docs.ultralytics.com/modes/track/) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.
## Advantages of Speed Estimation?
- **Efficient Traffic Control:** Accurate speed estimation aids in managing traffic flow, enhancing safety, and reducing congestion on roadways.
- **Precise Autonomous Navigation:** In autonomous systems like self-driving cars, reliable speed estimation ensures safe and accurate vehicle navigation.
- **Enhanced Surveillance Security:** Speed estimation in surveillance analytics helps identify unusual behaviors or potential threats, improving the effectiveness of security measures.
## Real World Applications
| Transportation | Transportation |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------:|
| ![Speed Estimation on Road using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c8a0fd4a-d394-436d-8de3-d5b754755fc7) | ![Speed Estimation on Bridge using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cee10e02-b268-4304-b73a-5b9cb42da669) |
| Speed Estimation on Road using Ultralytics YOLOv8 | Speed Estimation on Bridge using Ultralytics YOLOv8 |
!!! Example "Speed Estimation using YOLOv8 Example"
=== "Speed Estimation"
```python
from ultralytics import YOLO
from ultralytics.solutions import speed_estimation
import cv2
model = YOLO("yolov8n.pt")
names = model.model.names
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
# Video writer
video_writer = cv2.VideoWriter("speed_estimation.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
line_pts = [(0, 360), (1280, 360)]
# Init speed-estimation obj
speed_obj = speed_estimation.SpeedEstimator()
speed_obj.set_args(reg_pts=line_pts,
names=names,
view_img=True)
while cap.isOpened():
success, im0 = cap.read()
if not success:
print("Video frame is empty or video processing has been successfully completed.")
break
tracks = model.track(im0, persist=True, show=False)
im0 = speed_obj.estimate_speed(im0, tracks)
video_writer.write(im0)
cap.release()
video_writer.release()
cv2.destroyAllWindows()
```
???+ warning "Speed is Estimate"
Speed will be an estimate and may not be completely accurate. Additionally, the estimation can vary depending on GPU speed.
### Optional Arguments `set_args`
| Name | Type | Default | Description |
|---------------------|-------------|----------------------------|---------------------------------------------------|
| reg_pts | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
| names | `dict` | `None` | Classes names |
| view_img | `bool` | `False` | Display frames with counts |
| line_thickness | `int` | `2` | Increase bounding boxes thickness |
| region_thickness | `int` | `5` | Thickness for object counter region or line |
| spdl_dist_thresh | `int` | `10` | Euclidean Distance threshold for speed check line |
### Arguments `model.track`
| Name | Type | Default | Description |
|-----------|---------|----------------|-------------------------------------------------------------|
| `source` | `im0` | `None` | source directory for images or videos |
| `persist` | `bool` | `False` | persisting tracks between frames |
| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' |
| `conf` | `float` | `0.3` | Confidence Threshold |
| `iou` | `float` | `0.5` | IOU Threshold |
| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
| `verbose` | `bool` | `True` | Display the object tracking results |