Human Object Detection With Tfod.

Last Updated on May 3, 2021

About

Designed a Human object detection model with TFOD, to detect every human being possible in a frame.

The algorithm, which has been used is faster Rcnn for localisation of the object(i.e. Human) and inception net V2 for recognisation.

We collected our custom dataset and labelled the dataset with LABELIMG Tool.

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Tictactoe-Ai

Last Updated on May 3, 2021

About

The game is the traditional TicTacToe game in which a player has to make a pattern of 3 symbols which can be horizontal , vertical or diagonal. It can be played in the terminal or powershell in windows. It has 3 types of game play:

  1. with another human player.
  2. with random computer player in which you can win.
  3. with smart computer player in which you can never win.


The smart computer player is written using minimax algorithm which is used in AI.

Minimax is a decision rule used in artificial intelligence, decision theory, game theory, statistics, and philosophy for minimizing the possible loss for a worst case scenario. When dealing with gains, it is referred to as "maximin"—to maximize the minimum gain.

Minimax is a kind of Backtracking algorithm that is used in decision making and game theory to find the optimal move for a player, assuming that your opponent also plays optimally. It is widely used in two player turn-based games such as Tic-Tac-Toe, Backgammon, Mancala, Chess, etc.


In Minimax the two players are called maximizer and minimizer. The maximizer tries to get the highest score possible while the minimizer tries to do the opposite and get the lowest score possible.

Every board state has a value associated with it. In a given state if the maximizer has upper hand then, the score of the board will tend to be some positive value. If the minimizer has the upper hand in that board state then it will tend to be some negative value. The values of the board are calculated by some heuristics which are unique for every type of game.


The random player chooses his move randomly using the random library in python.



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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

About

HCI stands for the human computer interaction which means the interaction between the humans and the computer.

We need to improve it because then only it would improve the user interaction and usability. A richer design would encourage users and a poor design would keep the users at bay.

We also need to design for different categories of people having different age,color,gender etc. We need to make them accessible to older people.

It is our moral responsibility to make it accessible to disabled people.

So this project tracks our head ,eye and iris to detect the eye movement by using the viola Jones algorithm.But this algorithm does not work with our masks on as it calculated the facial features to calculate the distance.

It uses the eucledian distance to calculate the distance between the previous frame and the next frame and actually plots a graph.

It also uses the formula theta equals tan inverse of b/a to calculate the deviation.

Here we are using ANN algorithm because ANN can work with incomplete data. Here we are using constructive or generative neural networks which means it starts capturing our individual images at the beginning to create our individual patterns and track the eye.

Here we actually build the neural network and train it to predict

Finally we convert it to mouse direction and clicks and double clicks on icons and the virtual keyboard.

As a contributing or moral individuals it is our duty to make devices compatible with all age groups and differently abled persons.

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Resume Up-Loader

Last Updated on May 3, 2021

About

Description:-

Ever you apply to an organisation with cv through mail but it might happen that specific organisation don't know that actually candidate need like job preference or type of job, so it get easier when we use this app called resume up-loader.

working model:-

It is my first self project using Django (python

framework) called Resume Up-loader .

where you put every detail about yourself ,job location photos,signature,CV,after submitting the information load on the server and next page you can look all your information and download the Resume also ,i am continuously working on it and upgrading that it list all the company on that preference job location for your current qualification and skill it help the candidate to know in which company is he/she is suitable for and it also company to know their candidate batter


Under a projects section

To make this single page website I have use the python web framework called Django

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.

I have also use HTML to define the structure of front-end and use style tag to make this beautiful

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Go Alexa Go

Last Updated on May 3, 2021

About

GO ALEXA GO was created with millennials in mind. Why drive and text when you can just text and have Alexa drive?

Inspiration

We were inspired by Millennials' dangerous texting and driving habits, so we developed a driving system to allow them to text and still drive at the same time.

What it does

Our HTC Vive virtual reality experience allows the user to issue commands to our taxi driver, Alexa, and explore Sponsorville.

How we built it

We built our HTC Vive VR experience in Unity using C# and our Amazon backend with node.js and the Alexa skillset. The Amazon Alexa is able to take a user's directional input voice command through Amazon's unique browser-based web services built with node.js, and notifies Unity of the user's input with a web API hosted on Microsoft Azure.

Challenges we ran into

The first challenge we ran to was division of work. Charlie became our Unity/C#/HTC-Vive programmer, Randy became our impromptu Scrum Master/Front-End Designer/3D-modeler, and Caleb and Colin worked on node.js/Azure-IoT/Amazon Web Services. After we had a better sense of everyone's skill-set and strengths, we were able to snowball each other consistently throughout the course of the hackathon. Regarding Unity and C#, we ran into rigidbody and trigger debugging issues early on. With Alexa, we had troubles getting the browser based web service to work with node.js/Azure but by the middle of the second day, we were able to create a working prototype.

Accomplishments that we're proud of

Getting an Amazon Alexa to take voice commands and convert them to directional output in a Unity VR environment.

What we learned

Make sure you go into a hackathon with your division of work ready between your teammates. Additionally, make sure you teammates actually have a solid background in coding the work that is handed to them. Get together with your teammates every few hours, AGILE style, and see what progress has been made and if anyone needs help. Make sure everyone on your team can at some point handle paperwork because there will be a good amount of it throughout the course of the hackathon from the gathering of your teammates, to the final 12 hours before showtime. There needs to be a HUGE sense of trust between you and your teammates. Without some form of solid workflow (we used 2-hour scrums), you can run into problems like people just going off and coding who knows what for 3-4 hours of your hackathon before you realize you have issues.

What's next for Go Alexa Go

We plan on buying our own private islands and moving there with our solid-gold rocket ships from the amount of sponsorship money we've made from our amazing SponsorVille sponsors at Spartahack 2017.

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Vehicle Density Based Traffic Optimization/ Management (Ml And Iot)

Last Updated on May 3, 2021

About

PROBLEM STATEMENT:

As the vehicle population is rapidly increasing day by day, the cities are facing a huge traffic issue. We know how a traffic system work in current days. Even though there are no vehicles on the green side at the junction, a vehicle on the red side has to wait till the given time. This system has a drawback of traffic delay. On an average most of the junctions in the cities and towns facing this issue

ABSTRACT:

To overcome this problem of over delaying in the traffic, IoT plays a major role. Our idea is: placing IoT sensors at a distance of 100 meters range from the junction we can calculate an average count of vehicles on all the sides. Using this count as main characteristic the traffic light works dynamically that is the roads having high to low count of vehicles has green signal in decreasing order. To increase efficiency of vehicle count we implemented vehicle count using image processing

Tools:

1. Raspberry Pi

2. Camera (web cam)

3. Spyder.

Languages:

1. Python

  

           Vehicle count using image processing

 

Working:

•       At the junction the vehicles coming from different directions may face traffic issues.

•       By image processing technique we can capture the video of number of vehicles coming in different directions.

•       By connecting raspberry pi we can get the count of vehicles in different directions at the junction.

•       Based on the count obtained from different directions the traffic is cleared as per the maximum count of vehicles.

•       The traffic system is prioritized in decreasing order of the count of vehicles ie, the road with high vehicle count will be given first priority and so on.

•       The input in the form of video is captured by web cam, After the vehicle count is done, the control of traffic lights is done using Raspberry Pi

Advantages in real life:

1.Optimizing traffic inconsistency

2.Reducing air pollution

    3.Reducing noise pollution

    4.Priority traffic control

 

My role in the project:

This is a project for HACKATHON where we team of 8 members worked for 2 days. I worked mostly on the coding part for video processing in the project, I also developed an optimized code for prioritizing the traffic lights, this project contains various code segments one for image processing using cv module in python, one for vehicle count and other for prioritization.

More Details: Vehicle Density Based Traffic Optimization/ Management (ML and IoT)

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