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Project Report Paper

Words: 1691, Paragraphs: 98, Pages: 6

Paper type: Report , Subject: Artificial Intelligence

Categories: Artificial Intelligence

Table of Contents

CHAPTER-1

INTRODUCTION

Project Details

1.1.1 Project Definition

The main objective of virtual little one analyser is to monitor a child and prevent the child from doing any dangerous activity if parents are not around.

Purpose

The whole system will be installed in the local system of the office or any place.

All the detection will be done on the local system, so there won’t be any transfer of real-time data on servers.

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At initial level, the photo of the child (breach) will be shared to the concerned person remotely (only if opted).

If the concerned person wants to see the real-time video, then that video will be securely shown to the person through AWS.

After the concerned person selects to close the detection, the video sharing will be stopped and just detection will be continued at the location.

Scope

Many families are there where both the parents are working and they can’t keep eye on the children so to keep the child safe we have introduced this system.

By using this presence of any unwanted or harmful objects or person can be detected easily.

More importantly, our project gives real-time notification alert and real-time video streaming to the concerned person, so that the child can be prevented at the same time.

There are many security cameras available in the market. But none of them provides real-time child detection which is the main concern of the security.

Objective

Real-time breach notification.

Real-time anomaly detection.

Local system functionality.

Real-time video streaming.

Face Detection

Face Recognition

Movement detection alert

Tools and Technology

Hardware Specification

HDD: 2TB and above

Operating System: Windows, Linux, macOS

Processor: Intel Core i7-8700K or AMD equivalent, 3.7GHz or better

RAM: 8GB

Platform

Anaconda

Visual Studio Code

Programming Language Used

Python

Libraries

OpenCV

DLIB

Algorithms

Person Detection

Darknet 53

Pros: Fast and better accuracy

RCNN

SSD

Face Detection

DLIB

Pros: Better accuracy, scaling and side face detection

OpenCV

Tiny Tensorflow

Face Recognition

DLIB

Pros: Better accuracy, scaling and side face detection

CNN

Technology Used

AWS

Deep Learning

Software Tools

Database MySQL

About Anaconda

Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.

About Visual Studio Code

Visual Studio Code is a source-code editor developed by Microsoft for Windows, Linux and macOS. It includes support for debugging, embedded Git control and GitHub, syntax highlighting, intelligent code completion, snippets, and code factoring.

Literature Review

The term “Anomaly” refers to the presence of any unwanted or harmful thing or person in any restricted or secret place. My project “Anomaly Detector” is based on detection of anomaly. By using this presence of any unwanted or harmful objects or person can be detected easily. More importantly, my project gives real-time notification alert and real-time video streaming to the concerned person, so that the anomaly can be prevented at the same time. Moreover, my project also gives complete privacy against data-breach as this system will be installed completely on local system rather than online platform.

There are many security cameras available in the market. But none of them provides real-time anomaly detection which is the main concern of the security. Looking at this main lack of feature in the present system I have made this project which can be useful in all the scenarios from office to home to schools too.

Customers are more likely to use the service if the product:

Has real-time detection.

Has real-time security-breach notification

Has real-time video streaming.

Has face-detection and face-recognition support.

Movement detection support.

Has least probability of data leakage.

Thus this provides much secured system for security.

CHAPTER-2

Design: Analysis, Design Methodology and Implementation Strategy

2.1 AEIOU Summary Frame Work

Fig. 2.1 AEIOU Canvas

Activity

Monitoring Child

Update Information

Create website

Sending notification

Alerting parents

Safety measures

Environment

Noisy

Kitchen

Balcony

Terrace

Interactions

Sibling

Child

Parents

Guardians

Objects

Camera

Mobile

Computer

Buzzer

Users

Buyer

Seller

Guardians

Parents

Children

Stakeholders

Developers

2.2 Mind Mapping Canvas

Fig. 2.2 Mind Mapping Canvas

2.3 Empathy Mapping Canvas

Fig. 2.3 Empathy Mapping Canvas

Users

Parents

Buyers

Sellers

Children

Stake Holders

Owner

Administrator

Developer

Activities

Update information

Child monitoring

Safety measure

Creating website

Sending notification

Story Boarding

Happy:

Happy:

Sad:

Sad:

2.4 Ideation Canvas

Fig. 2.4 Ideation Canvas

People

Stakeholder

Parents

Guardians

Buyer

Admin

Activity

Detecting Child

Capturing Images

Sending notification

Monitoring child

Situation/Context/Location

Balcony

Living room

Kitchen

Rooms

Pros/Possible Solutions

Face detection

More accuracy

Camera

Time saving

More efficiency

2.5 Product development canvas

Fig. 2.5 Product development canvas

Purpose

Child monitoring

Child safety

Parents relief

Time management

People

Parents

Children

Guardian

Developers

Product experience

Customer comfort

Child safety

Enhance customer experience

Product function

Quality Maintenance

Different types of alerts

Easy to keep eye on the child

Product features

On time notification

Customer satisfaction

Fast detection

Components

Python 2.7

Camera

My SQL database

Artificial intelligence

Customer revalidation

Late notification

Misunderstanding the child

Customer record

Reject, Redesign, Retain

Slow speed

On time notification

Incomplete information

User friendly

CHAPTER-3

ABOUT THE SYSTEM

3.1 System Requirement Specification

There are two types of requirements. First is functional requirements and the other is non functional requirement.

Non-functional requirements:

Database:

A data base management system that is available free of cost in the public domain should be used

Usability Requirement:

The system shall allow the users to access the system from the local system.

Availability Requirement:

The system needs to be available 100% for the user. The system shall be operational 24 hours a day and 7 days a week.

Platform: Accuracy:

Here the computer OS is required. The video or photos can be opened in AWS from any browser.

Accuracy:

The system should accurately provide real time information taking into consideration various concurrency issues.

Usability Requirement:

The system shall allow the users to access the system from the local system and in AWS from any browser.

Performance Requirement:

The system should give real-time anomaly detection and video streaming

Functional Requirements:

Registration:

User (admin) needs to register them in order to use the service.

Login:

The option is selected for login and the input will be entered by the user which will be username and password. Here, user can access the system if the password is valid else if password is invalid, the user is asked to re-enter the password.

Monitoring:

User will be able to monitor the activities of detection and notification.

Decision:

User will have the right to take the decision whether the anomaly detected is actual anomaly or allowed child.

Feasibility Study

A feasibility study is an analysis used in measuring the ability and likelihood to complete a project successfully including all relevant factors. It must account for factors that affect it such as economic, technological, legal and scheduling factors. Project managers use feasibility studies to determine potential positive and negative outcomes of a project before investing a considerable amount of time and money into it.

Technical Feasibility:

The proposed system is capable of holding data to be used.

The proposed system is capable of providing adequate response regardless of the number of users.

The proposed system is module to the administrator so more features in future can be added.

Operational Feasibility:

Customers here are the most important part of the system and the proposed system will provide a convenient mode of operation to them. The system will be available to the customers 24×7.

Economical Feasibility:

Easy maintenance of the system is possible.

Affordable pricing for operation.

3.3 Project Planning

Spiral model is one of the most important Software Development Life Cycle models, which provides support for Risk Handling. In its diagrammatic representation, it looks like a spiral with many loops. The exact number of loops of the spiral is unknown and can vary from project to project. Each loop of the spiral is called a Phase of the software development process. The exact number of phases needed to develop the product can be varied by the project manager depending upon the project risks. As the project manager dynamically determines the number of phases, so the project manager has an important role to develop a product using spiral model.

The Radius of the spiral at any point represents the expenses (cost) of the project so far, and the angular dimension represents the progress made so far in the current phase.

The Spiral model is called as a Meta Model because it subsumes all the other SDLC models. For example, a single loop spiral actually represents the Iterative Waterfall Model. The spiral model incorporates the stepwise approach of the Classical Waterfall Model. The spiral model uses the approach of Prototyping Model by building a prototype at the start of each phase as a risk handling technique. Also, the spiral model can be considered as supporting the evolutionary model – the iterations along the spiral can be considered as evolutionary levels through which the complete system is built.

CHAPTER-4

Analysis

4.1 ER Diagram

Fig 4.1 ER Diagram

4.2 Data Flow Diagrams

Fig 4.2 DFD Level 0

Fig. 4.3 DFD Level 1 Admin

Fig 4.4 DFD Level 1 User

4.3 Use Case Diagram

Fig. 4.5 Use Case Diagram

4.4 Sequence Diagram

Fig. 4.6 User Sequence Diagram

Fig. 4.7 System Sequence Diagram

4.5 Activity Diagram

Fig. 4.8 Activity Diagram

4.6 Class Diagram

Fig. 4.9 Class Diagram

CHAPTER-5

Design

5.1 System Flow Diagram

Fig. 5.1 System Flow Diagram

5.2 Data Dictionary

Sr. No. Field Data Type Constraints Description

1 Email-id Varchar(50) Primaty key Uniquely identifies user

2 First name Varchar(20) Not null Name of user

3 Last name Varchar(20) Not null Surname of user

4 Mobile number Varchar(13) Not null Mobile number of user

Table 5.1 Registermaster

Sr. No. Field Data Type Constraints Description

1 Email-id Varchar(50) Primaty key Uniquely identifies user

2 Password Varchar(15) Not null Password of user

3 Time of login Time Not null Time when user logins

Table 5.2 Loginmaster

Sr. No. Field Data Type Constraints Description

1 Recognised person Varchar(50) Not null Name of recognised person

2 Link of AWS Varchar(50) Primary key AWS link where video is stored

Table 5.3 Recognisatonmaster

Sr. No. Field Data Type Constraints Description

1 Detected object Varchar(50) Not null Type of detected object

Table 5.4 Detectionmaster

Sr. No. Field Data Type Constraints Description

1 Video name Varchar(15) Primaty key Unique name of video

2 Storage location Varchar(50) Not null Storage location of video

3 Date of recording Date Not null Date of recording

4 Time of recording Time Not null Time of recording

Table 5.5 Videomaster

CHAPTER-6

Conclusion and Future Work

Conclusion

This system first of all detects the change in frames at real-time. It tries to recognize the object detected. If the detected object is a person, it tries to detect the face of person. If the face is detected, it tries to recognize the face too.

If at any stage if the object is not recognized or face is not detected or the recognized person is a restricted person, then the system generates anomaly notification and creates video storing in local machine. It sends immediate video clip to the user also. If the user confirms it to be an anomaly, it sense footage at regular intervals to AWS till the user selects to stop it.

Future Work

As a future enhancement I will try to identify the types of bugs that can be occurred inside the development code.

Here there’s no SaaS module. In future I will implement SaaS module too.

As a future enhancement, I will also try to use more powerful algorithms for object detection, face detection and face recognition too.

References

Research Papers:

Object Detection Algorithms for Video Surveillance Applications by Apoorva Raghunandan, Mohana, Pakala Raghav, H. V. Ravish Aradhya

Rapid face detection and annotation with loosely face geometry by P. Shanmugavadivu, Ashish Kumar

3D human face recognition using point signature by Chin-Seng Chua, Feng Han, Yeong- Khing Ho

Websites

Learning OpenCv –

OpenCv in Face Detection –

About the author

This sample essay is completed by Harper, a Social Sciences student. She studies at the University of California, Santa Barbara. All the content of this paper is just her opinion on Project Report and should not be seen as the way of presenting the arguments.

Read other papers done by Harper:

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Project Report. (2019, Dec 19). Retrieved from https://paperap.com/project-report-109-best-essay/

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