Revision as of 23:03, 8 March 2015

The document provides a template of the Software Requirements Specification (SRS). It is inspired of the IEEE/ANSI 830-1998 Standard.

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Document History
	Version	Date	Authors	Description	Validator	Validation Date
	0.1.0	TBC	BODARD Christelle, QIAN Jean, ZOMINY Laurent	TBC	TBC	TBC

1. Introduction

1.1 Purpose of the requirements document

This Software Requirements Specification (SRS) identifies the requirements for the Autonomous and Mobile Video Control.

1.2 Scope of the product

This project is based on a robot named RobAir, supplied by a camera. The purpuse is to enable the robot to recognize a specific person and follow him; the list of people to be recognized will be sent by an Android app.

1.3 Definitions, acronyms and abbreviations

Android: The most widely used mobile OS. Here it is used to send image or video frame to the robot.
Face recognition: To automatically identify or verify a person from a digital image or a video frame from a video source
OpenCV: It is a library of programming functions mainly aimed at real-time computer vision, developed by Intel Russia research center in Nizhny Novgorod, and now supported by Willow Garage and Itseez.
Python: It is a widely used general-purpose, high-level programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java. Here it is used to implement the face recognition using OpenCV.

1.4 References

1.5 Overview of the remainder of the document

2. General description

2.1 Product perspective

Our system is divided into 3 parts:

The face images of target persons are taken and sent to the robot by the Android application.
The robot detects the target person among all the people within view of the cameras.
The robot automatically follow the target person.

2.2 Product functions

Update of the list of target persons on the fly: We can use the Android application to take photos of new target persons and send to the robot whenever we need.
Face recognition: The robot will automatically detect the target persons among all persons within the view of the cameras.
Follow: The robot will follow the persons whose image is sent by the Android app.
Resize: The robot will automatically resize the window to have focus on target visage.

2.3 User characteristics

The user needn't know anything! The robot will automatically follow the target person. He just has to take pictures about person's visage.

2.4 General constraints

La je sais pas quoi mettre

2.5 Assumptions and dependencies

The accuracy of face detection depends highly on two parts:

The quality of face samples
The efficiency and accuracy of the face detection algorithm.

3.Specific requirements, covering functional, non-functional and interface requirements

document external interfaces,
describe system functionality and performance
specify logical database requirements,
design constraints,
emergent system properties and quality characteristics.

3.1 Requirement X.Y.Z (in Structured Natural Language)

Function: Learn someone's face to be able to recognize this person and keep watching him .

Description:

Inputs: Faces pictures

Source: Android Smartphone

Outputs: Detection and recognition

Destination: Robot

Action:

Natural language sentences (with MUST, MAY, SHALL)

The user must take pictures about target people and sent them by our application to the database.

Graphical Notations : UML Sequence w/o collaboration diagrams, Process maps, Task Analysis (HTA, CTT)
Mathematical Notations
Tabular notations for several (condition --> action) tuples

Functional requirements:

Detect a face
Recognize the face
Track recognized face
Priority on faces for tracking

Non functional requirements:

Accuracy
Rapidity
Fluidness on the movement of the camera

Pre-condition:

Post-condition:

Side-effects:

@@ Line 59: / Line 59: @@
 ==2.3   User characteristics==
-The user needn't know anything! The robot will automatically follow the target person(user).
+The user needn't know anything! The robot will automatically follow the target person. He just has to take pictures about person's visage.
 ==2.4   General constraints==
+La je sais pas quoi mettre
 ==2.5   Assumptions and dependencies==
 The accuracy of face detection depends highly on two parts:
@@ Line 79: / Line 82: @@
 '''Description''':
-'''Inputs''': someone image
+'''Inputs''': Faces pictures
-'''Source''':
+'''Source''': Android Smartphone
-'''Outputs''': detection and recognition
+'''Outputs''': Detection and recognition
-'''Destination''':
+'''Destination''': Robot
 '''Action''':
 * Natural language sentences (with MUST, MAY, SHALL)
+* The user must take pictures about target people and sent them by our application to the database.
 * Graphical Notations : UML Sequence w/o collaboration diagrams, Process maps, Task Analysis (HTA, CTT)
 * Mathematical Notations
@@ Line 94: / Line 100: @@
 '''Functional requirements''':
-*Detect a face
-*Recognize the face
+* Detect a face
-*track the face recognized
+* Recognize the face
+* Track recognized face
-*priority on faces for tracking
+* Priority on faces for tracking
 '''Non functional requirements''':
-*accuracy
+* Accuracy
-*rapidity
+* Rapidity
-*fluidness on the movement of the camera
+* Fluidness on the movement of the camera
 '''Pre-condition''':
 '''Post-condition''':

Difference between revisions of "Proj-2014-2015-Regie Video Autonome Et Mobile Multicamera/SRS"