Object Tracking Using Computer Vision | Tensorflow


To Use Deep Learning to predict the current location of a Reinforcement learning agent based on point cloud (3D) data. A combination of CNN and RNN is used to determine the X, Y, and Z coordinates and an angle theta, which denotes the angle at which the object is moving.

Digital Driver


Reinforcement Learning has become a primary driver for autonomous robots, be it person bots that acts as smart pets like Anki's Cozmo or fancy robots that are present in hotels for food delivery.


The current state or location of these agents is harder to describe with the use of GPS coordinates. Since these agents are meant to perform in a given closed environment, We can use visual information they are collected from the agent's camera to predict the current state coordinates.


This technique is called Visual Odometry wherein we use Convolution and Recurrent Neural Networks to process the 3D frame to determine the current location.

Business Value


Visual Odometry can be applied in a situation where GPS coordinates are hard to determine.


This provides the Agent with a much better understanding of its surroundings to take necessary actions.


It is widely used in Autonomous driving cars, personal robots, etc.


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