Traffic Analyzer

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Instructions
Input - Click on the “+” button in the video player and upload a decent resolution image or video with less than 40 MB.
Output - Inferred with the detection of vehicles, pedestrians and road signs in real time.
Note: Upload the appropriate video for optimal performance. Video that doesn't relate to the model might provide you unexpected results.

With the installation of traffic surveillance cameras in recent years, a massive archive of traffic video footage has been created. From traffic dispersal to autonomous driving, the ongoing increase in video data can be used in a variety of ways. Study and implementation of relevant algorithms will bring governance in the smooth traffic flow across the cities saving huge amounts of time, fuel and naturally making the world much more eco friendly. In addition, this can be used to identify the vehicle locations, number of vehicles, pedestrians as well as road signs to autotomize the vehicles by making intelligent decisions

Applications like smart vehicle navigation and pedestrian safety need a comprehensive understanding of what happens on roadways. Analyzing the trends of traffic may also help in planning more efficient road networks and road maintenance. It is of utmost importance to enable the AI algorithms and make them more precise and efficient as we live in the autonomous vehicles world.

Use Cases

Traffic Flow Management, Vehicle Navigation, Pedestrian Safety, Road Engineering, Road Network Management.

API Request and Response

TensorGo Platform provides you with a one stop solution to customize the our API offerings as per your use case by mixing and matching the existing APIs or requesting for a new custom model. This accelerates the development of use cases with minimal or no code towards deep learning applications.
The endpoint for API is:
[URL]
Request:
post/
[URL]
	body:{
		id:”Some Unique  ID”
		app:”Name of the app”
		file:”Attached File”
              }
Example:
post/
	[URL]
	body:{
		id:xsk231ds168wd
		app:trafficCamNet
		file:”Attached File”
              }

Response:
	{ msg:Uploaded }

Download the Uploaded video:
get/
	[URL]
	body:{
		id:xsk231ds168wd
		app:trafficCamNet
             }