International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
A smart target detection system using Fuzzy Logic and Background Subtraction Madhurika Ubhe1, Deepak Parashar2, Prof. U. A. Jogalekar3, Vaishali Godse4, Kshitija Jarhad5 1,2,4,5 Students,
3Assistant
Computer Department, Smt. Kashibai Navale College of Engineering, Pune-411041 Professor, Computer Department, Smt. Kashibai Navale College of Engineering, Pune-411041
---------------------------------------------------------------------***---------------------------------------------------------------
Abstract - In the proposed system, fuzzy logic has been used
found, the speed of execution is an issue of concern. Since the complex image processing operations take an acceptably large amount of time to be performed, software’s which are robust to the complexities of image processing and at the same time meet the “instant result” criterion of today’s users , are the need of the present scenario.
to model a robust Background Subtraction method for object detection. We first separate the background from the input static video using fuzzy logic. For this, the first few frames of the input video are used as a template for the background. After this, the objects in motion are identified and then, whether the moving object is a car or human is inferred based on the aspect ratio of the moving object. We implement target detection in static videos using background subtraction and fuzzy logic. In this project, we are using humans and cars as our targets. We first identify our targets - cars/bikes and/or humans if they are present in the video and then discriminate between the two to show accuracy i.e. whether the system can differentiate between cars/bikes and human. Thus, target detection as well as identification can be implemented using the proposed system.
We present a novel approach to detect targets in static videos using Fuzzy Logic and Background Subtraction. The intricacies of the proposed system will make it clear that we have wisely exploited the nature of surveillance applications. We are working only with humans and cars as our targets, but animals, or other immobile objects can also be used in place or in conjunction with humans and cars.
Key Words: Detection, Background Subtraction, Fuzzy Inference System, Fuzzy Logic, Background Modeling, Moving Object Detection, Aspect Ratio.
1.2 Overview of our Target Detection System
1. INTRODUCTION
As is seen the architecture, first a video is given as input to our system. The video may be captured through a live camera or it may be an already recorded static video.
1.1 About Target Detection and our System Target detection has become increasingly important with respect to various security applications like bank surveillance, traffic surveillance, and residential area surveillance to check for intruders, etc.
First, the frames are extracted from the video using a suitable frame extracting software. Denoising techniques are then applied to the frames to reduce blurring. This helps in accurate pixel intensity calculation so that there is little confusion in classification of pixels in the later stages.
Target detection has many applications apart from the security perspective but we chose to exploit its uses for security related applications like surveillance and remote monitoring, because it a crucial need of the present society where bank robberies, residential thefts, illegal hunting, industrial espionage, etc. are rampant.
Then, RGB (Red Green Blue) values of each pixel are through RGB channel separation. The first N frames of the video are used as a template for background modeling. The larger the value of ‘N’, the more accurate will be the classification of background pixels. The redundancy of the intensities of the pixels in the ‘N’ frames is captured and it is used as a model for the background. But the major requirement here is that the target object should not be present in the first ‘N’ frames that we have chosen for background modeling. The background model is prepared by applying fuzzy logic on the RGB values obtained. The output after applying fuzzy logic on the pixel intensities is a fuzzified value for each pixel. We can then set thresholds
Target detection in images and videos involves image processing. Various image processing techniques such as denoising, edge detection, background elimination and many others come into play when target detection in videos and images is to be performed. The requirement of all these techniques makes target detection a bit complicated to implement for mediocre developers and even if a successful implementation is
© 2017, IRJET
|
Impact Factor value: 5.181
|
ISO 9001:2008 Certified Journal
|
Page 2264