Vehicle Object Detection Using Computer Vision In Highway Scene

Last Updated on May 3, 2021

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Database:              MongoDB

API used:                Tensorflow Object Detection API (1x), FLASK Frame Work

DL Algorithms:     SSD with Inception V2


Data Gathering of vehicles from  COCO (Common Objects in Context) dataset, Data Augmentation, Data Annotation(Labeling image using labelim tool), convert XML to csv, Generate tf record, Model Training using pre trained model SSD and generate pb model after 5000 epochs and prediction of vehicles and saving the predicted image. Stored all training logs and predictions logs in the MongoDB 

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Human Computer Interaction Using Iris,Head And Eye Detection

Last Updated on May 3, 2021

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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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Artificial Neural Network

Last Updated on May 3, 2021

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What is ANN?

An artificial neural network (ANN) is the component of artificial intelligence that is meant to simulate the functioning of a human brain. Processing units make up ANNs, which in turn consist of inputs and outputs. An artificial neural network (ANN) is the component of artificial intelligence that is meant to simulate the functioning of a human brain. Processing units make up ANNs, which in turn consist of inputs and outputs.


The input values are processed through all these hidden layers to get the output value just like in human brain.

Neurons are basically building blocks of ANN as the main aim of ANN is to recreate neuron

Dendrites = receiver of signals

Axon is transmitter of signals .

Neurons communicate with one another at junctions called synapses. At a synapse, one neuron sends a message to a target neuron—another cell. Most synapses are chemical; these synapses communicate using chemical messengers. Other synapses are electrical; in these synapses, ions flow directly between cells.

Perceptron is a neural network unit that does contain computations to detect features in data basically artificial neuron.

A cost function is a single value, not a vector, because it rates how good the neural network did as a whole.

Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.

Problem description: Business problem on real world problem. In the dataset we have 10000 observations of approx 6 months with 14 columns about their information related to bank. RowNumber, CustomerId, Surname, CreditScore, Geography, Gender, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, Exited( dependent variable) whether the person stayed in the bank or left the bank 1 = left , 0 = stayed. So we have to understand the co relation between all the features and exited , now based on this dataset the bank wants to understand why people are preferring/ not preferring their bank as they want maximum customers in their bank . so our trained model will predict whether any new customer will leave the bank or not so that the bank can give some special offers to them so that they stay. We have to train the dataset then deploy the model on future customer by predicting the probability.


TABLE OF CONTENTS:

Importing the libraries

Part 1 - Data Preprocessing

Importing the dataset

Encoding categorical data

Splitting the dataset into the Training set and Test set

Feature Scaling

Part 2 - Building the ANN

Initializing the ANN

Adding the input layer and the first hidden layer

Adding the second hidden layer

Adding the output layer

Part 3 - Training the ANN

Compiling the ANN

Training the ANN on the Training set

Part 4 - Making the predictions and evaluating the model

Predicting the result of a single observation

Predicting the Test set results

Making the Confusion Matrix

 

DATASET