Case Study: Smart Surveillance System

OVERVIEW

Automatic monitoring is essential to achieve a more secure surveillance system. To build a system of such kind machine should get trained with more authentic data.

Challenges

• Provide a surveillance system which is more robust and secure.

Solution

• For any surveillance system to be more robust and secure, initially it must focus on high priority items such as observation of the area. We are currently limiting this observation set up only on Humans.
• Multiple angles will be captured. ML model gets trained for categorizing the individuals.

Technology Used

• Tensor Flow, Python Django and DRF.