The project proposes to show the benefits of a smart house . Details about the technical substructure and application of the designed home automation system through Gesture Recognition are described. This project presents the extension of existing vision based gesture recognition system using an algorithm that combines the classical and novel approach for gesture recognition. Under future work, several improvements and support structures may be implemented in order to increase the capabilities and the functionality of the system

1. SMART AND EFFICIENT HOME AUTOMATION USING
HAND GESTURE
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
UNIVERSITY COLLEGE OF ENGINEERING ,KARIAVATTOM
PRESENTED BY
MOHAMMED ST
ASWIN DEV
ABHISHEK NAIR
THEJASWARRIER

2. INTRODUCTION
• The Aim of project is to implement image processing
algorithms for home automation purposes effectively for
smarter control of home.
• Our project focuses on real time image processing
algorithms that can be used to detect, track objects and
persons and can also recognize their activities and
gestures in indoor scenarios.
• This project demonstrates home lighting and Fan control
where these algorithms can be used to achieve effective
control

3. GESTURES USED HERE

4. BLOCK DIAGRAM

5. • Video content analysis and
capturing image
• color space conversion from RGB
to HSV Image
• Filtering of image

6. • Image enhancement and skin color
Detection
• Image segmentation and finding
centroid
• Counting the number of segments

7. • Defining function to each number
• Communication with hardware module
using serial port
• RS232 is used to convert input signal to
TTL compatible signal
• Programming microcontroller to drive the
led and fan

8. • RGB
• Normalized RGB
• HSV
• YcrCb
Different Color Models

9. • High cost of conversion
• Based on intuitive values
• Invariant to highlight at white light sources
• Pixel with large and small intensities are
discarded as HS becomes unstable.
• Can be 2D by removing the illumination
component
HSV

10. MAIN HARDWARE REQUIRED
• Microcontroller
• Max 232
• Camera
• Transistor
SOFTWARE USED
• MATLAB & Simulink Release 2013a
• MPLAB

11. HARDWARE DESCRIPTION

12. Hardware details
Microcontroller:PIC16F877
• Operating speed: DC - 20 MHz clock input
DC - 200 ns instruction cycle
• Up to 8K x 14 words of FLASH Program Memory
• Up to 368 x 8 bytes of Data Memory (RAM)
• Up to 256 x 8 bytes of EEPROM Data Memory
• Watchdog Timer (WDT) with its own on-chip RC
oscillator for reliable operation
• Programmable code protection
• Power saving SLEEP mode
• Selectable oscillator options
• Low power, high speed CMOS FLASH/EEPROM
technology
• Processor read/write access to program memory
• Wide operating voltage range: 2.0V to 5.5V

13. Pin Diagram

14. Camera
• Resolution: 3.5+MP camera

15. MAX232
• MAX232 device is a dual driver/receiver that includes a
capacitive voltage generator to supply TIA/EIA232F voltage levels
from a single 5V.
• Each receiver converts TIA/EIA-232-Finputs 5-V TTL/CMOS
levels.
• Operates up to 20kbit/s
• Two Drivers and Two Receivers

16. Transistor
• Switching and Amplifier
• High- Voltage, VCEO = 65 V
• Low-Noise

17. ADVANTAGES
• Gestural control is quick and convenient to
use. Unlike touchscreens, cell phones,
remote controls, or other manual
• Modes of operation gestural control is
completely hands-free
• Attractive

18. • Easy to Operate
• Technology of the future

19. DISADVANTAGES
• Doesn’t have as many features as
remote
• Shadow problem

20. • Thus from the goal of project scope can be described as
detailed study of widely used real-time image processing
algorithms
• Analysis of their efficiency and usability for indoor applications
and changing or adapting these algorithms for such
applications.
• OUR project focuses on development of efficient methods by
which real-time image processing algorithms can be modified
for use in home automation and smart home control.
Goal of the Project

21. [1] Phung, S.L., Bouzerdoum, A. ; Chai, D., “Skin segmentation using
color and edge information,” Signal Processing and Its Applications,
2003. Proceedings. Seventh International Symposium on (Volume:1
)1-4 July 2013
[2] Alon, Jonathan; Athitsos, V.; Quan Yuan; Sclaroff, S. "A Unified
Framework for Gesture Recognition and Spatiotemporal Gesture
Segmentation", Pattern Analysis and Machine Intelligence, IEEE
Transactions on, On page(s): 1685 -1699 Volume: 31, Issue: 9, Sept.
2009
REFERENCE

22. THANK YOU