Traffic Anomalies Computer Vision Model
How to use the Traffic Anomalies Detection API
Try This Model
Drop an image here or click to upload
Code Snippets
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="traffic-anomalies/7")Or, Use Free Motorbike and Human Detection API
Powered by general detection model
pip install inference-sdk# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workspace
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="<YOUR_WORKSPACE>",
workflow_id="<YOUR_WORKFLOW_ID>",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"classes": "motorbike, human"
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Run on custom image
Drop an image here or click to upload
About Traffic Anomalies Model
Here are a few use cases for this project:
-
Use Case: Traffic Monitoring Systems The "Traffic Anomalies" model could be employed in traffic monitoring systems to detect, record, and alert operators about any unusual activity, such as a pedestrian walking on highways or motorbikes traveling in inappropriate areas. This will help improve safety conditions and optimize traffic flow.
-
Use Case: Autonomous Vehicles This model could be integrated into autonomous driving systems, aiding in the identification and differentiation between motorbikes and humans. The classification could help autonomous vehicles to drive more accurately and safely.
-
Use Case: Traffic Rule Enforcement The model can be used by traffic law enforcement to detect violations like motorbikes driving on sidewalks or pedestrians entering prohibited areas. By identifying these violations automatically, law enforcement could better ensure public safety.
-
Use Case: Urban Planning Urban planners could use the model to understand the behavior of motorbikes and pedestrians in different areas of the city. This information could then be used to create safer and more efficient transport infrastructure.
-
Use Case: Entertainment and Sport Events For motorbike races or stunt events, this model could be used to analyze riders' performance, track their movements, and even score stunts. Moreover, it could spot any danger in real-time, like a pedestrian unintentionally entering the performance zone.
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ traffic-anomalies_dataset,
title = { Traffic Anomalies Dataset },
type = { Open Source Dataset },
author = { traffic },
howpublished = { \url{ https://universe.roboflow.com/traffic-pagod/traffic-anomalies } },
url = { https://universe.roboflow.com/traffic-pagod/traffic-anomalies },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { jan },
note = { visited on 2026-07-29 },
}