People Segmentation

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Instructions
Input - Click on the “+” button in the video player and upload a decent resolution video with less than 40mb.
Output - Inferred video with segmented people and their count over a time in JSON format.
Note: Upload the appropriate video for optimal performance. Video that doesn't relate to the model might provide you unexpected results.

In computer vision, image segmentation refers to the technique of grouping pixels in an image into semantic areas typically to locate objects and boundaries. People segmentation model is trained to detect the people in a crowd or individual locations. This model segments the people from the background scene and draws boundary boxes around each person as per their physical personalities.

Self-governing video evaluation systems have become increasingly important in recent years. There are numerous applications related to intelligent video surveillance systems such as people counting/detection, facial recognition, vehicle detection. It enables continuous monitoring of human actions which allow tagging of human body parts such as head, arms, torso and legs to achieve activity recognition tasks. The ability to track person movement in crowds through people segmentation models flourish the crowd tracking analyses and infinite possibilities while combined with other neural networks.

Use Cases

Photography Editing, Augmented Reality, Artistic Effect, Crowd Analysis, Surveillance Systems.

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]
Example:
post/
[URL]
	body:{
		id:”Some Unique  ID”
		app:”Name of the app”
		file:”Attached File”
              }
Example Usage:
post/
	[URL]
	body:{
		id:xsk231ds168wd
		app:peopleSegNet
		file:”Attached File”
             }

Response:
	{ msg:Uploaded }

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