Object Tracking Using Computer Vision | Tensorflow

Overview

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

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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.

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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.

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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

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

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

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

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