Low Cost Smart Cam Design full presentation
Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
project report tiger
Active In SP
**

Posts: 1,062
Joined: Feb 2010
#1
08-02-2010, 10:27 AM



.ppt   Low Cost Smart Cam Design_final ppt.ppt (Size: 781.5 KB / Downloads: 133)

Low Cost Smart Cam Design


1.Introduction

This paper proposes a very low cost vision system that is able to perform image processing tasks with certain processing algorithms.

Digital cameras include dedicated digital image processing chips to convert the raw data from the image sensor into a color-corrected image organized in a standard image file format.

The images flow will be also enriched with a collection of information that ranges from the position of recognized objects to their color, size or shape.



The main contributions of the proposed work are the following: - The study and development of a very low cost
architecture for digital image processing; - The implementation of a set of optimized digital image processing algorithms to be used;
- The development of a color vision system that has been successfully applied to real-world case studies.



2.Previous Work



This camera has been developed in three different versions: CMUcam1, CMUcam2 , and CMUcam3.

CMUcam1 is a low-cost, low-power sensor for mobile robots to perform many different kinds of real-time processing, with a maximum resolution of 80x143 tracking 17 frames per second.

The CMUcam2 is characterized by a maximum resolution of 176 x 255 and it is able to track user dened color blobs up to 50 frames per second and it is also able to track motion using frame differencing at 26 frames per second.


CMUcam3 provides a exible and easy to use open source development environment. It supports CIF with resolution of 352x288 and is able to process images at a rate of 26 frames per second.



3.Digital Image Processing Algorithms

The following algorithms have been implemented in order to perform the image processing:

Background subtraction and foreground segmentation .
Blob detection .
Object tracking .





1. Background subtraction and foreground segmentation

This algorithm allows to obtain an image in which the background has been completely removed.
This elaboration can be performed both in a static scenario and in a dynamic one.




2. Blob Identication and Detection

The algorithm is represented in 2 phases:

In the rst phase an elaborated image can be processed in order to¦

- assign to adjacent pixels belonging to the same objects the same identication number;

- Lists of identication numbers refer to blob.

The second phase performs a second scan of the image, in order to¦

- map all the identication numbers that refer to the same object to the same blob identication letter.






3. Object Tracking

The rst step of the proposed object tracking algorithm is the evaluation of the list of blobs from detection phase.

- If this list is empty ,then it is possible to generate the output.
- Otherwise a blob of list has to be selected and evaluated , with respect to its color, shape, position, etc...

If the selected blob has been detected to be the primary target then it is possible to update the currently selected primary target and to remove the blob from the blobs list.

Otherwise, if the target has been detected to be a secondary target, it is possible to update the secondary targets list and to remove the blob from the blobs list.

The rst step is the detection of adjacent pixels belonging to the same objects as stated:

the pixel on the top left corner of the image will receive either a 0, if it belongs to the background, or a 1, if it belongs to an object;

any other pixel of the rst top row will receive:
a 0 if belonging to the background;
the same value of the adjacent pixel on its left if both the pixel on its left if both the pixels belong to an object;
new value if it belongs to an object but the adjacent pixel on its left belongs to the background ;
any other pixel of the other rows will receive:

a 0 if belonging to the background;
the same value of the adjacent pixel on its left if both the pixels belong to an object;
the same value of the adjacent pixel on its top if both the pixels belong to an object, but the adjacent pixel on the left of the lower one belongs to the background;
a new value (never used before on the same image) if it belongs to an object but the adjacent pixel on its left belongs to the background.

Figure 4.2 shows the result.



5. Experimental Results

One of the real scenarios in which the proposed low cost smart cam has been exploited in order to perform a recognition task is the test-tubes detection task.

The following couple of experiments with test-tubes are presented:

Detection of test-tubes presence or absence.
Tracking of the desired test-tube.



6. Conclusions

Solution for real-world case studies, since it is able to correctly detect, recognize and track the desired object.

The proposed smart cam is comparable to the previous models from the technological and the functional point of view,

Effective low cost solution for the detection and the tracking of objects, based on the elaboration on their color, position, shape and size.
Reply

Important Note..!

If you are not satisfied with above reply ,..Please

ASK HERE

So that we will collect data for you and will made reply to the request....OR try below "QUICK REPLY" box to add a reply to this page

Quick Reply
Message
Type your reply to this message here.


Image Verification
Please enter the text contained within the image into the text box below it. This process is used to prevent automated spam bots.
Image Verification
(case insensitive)

Possibly Related Threads...
Thread Author Replies Views Last Post
  DESIGN ATM CONTROLLER pdf seminar flower 1 963 22-07-2016, 02:13 PM
Last Post: mkaasees
  Development of Smart Antenna Technology project uploader 1 714 07-04-2016, 11:24 AM
Last Post: mkaasees
  AN OVERVIEW OF SMART ANTENNA TECHNOLOGY FOR MOBILE COMMUNICATIONS SYSTEMS seminar addict 3 1,534 07-04-2016, 11:24 AM
Last Post: mkaasees
  smart note taker wikipedia jaseelati 0 326 22-01-2015, 04:49 PM
Last Post: jaseelati
  smart traffic management system ppt jaseelati 0 233 27-12-2014, 03:45 PM
Last Post: jaseelati
  low power and area efficient carry select adder ppt padmajaece405 1 418 15-10-2014, 03:02 PM
Last Post: mkaasees
  witricity full report project report tiger 28 38,162 30-08-2014, 02:26 AM
Last Post: radiopodarok.ru
  DESIGN AND IMPLEMENTATION OF FASTER AND LOW POWER MULTIPLIERS seminar flower 1 735 26-07-2014, 01:00 AM
Last Post: Guest
  ACCIDENT PREVENTION USING WIRELESS COMMUNICATION full report computer science topics 5 7,597 17-04-2014, 11:07 AM
Last Post: seminar project topic
  silicon on plastic full report computer science technology 2 2,949 13-04-2014, 10:34 PM
Last Post: 101101