Shaping the Future of ICT

Trends in Information Technology, Communications Engineering, and Management

Ibrahiem M. M. El Emary, Anna Brzozowska, Ibrahiem M. M. El Emary, Anna Brzozowska

• Publisher: CRC Press
• Year: 2017
• Print ISBN: 9780367573386
• eBook ISBN: 9781351651295
• Edition: 1
• Topic: Computer Science
• Subtopic: Information Technology
• Index: Computer Science

The International Conference on Communications, Management, and Information Technology (ICCMIT'16) provides a discussion forum for scientists, engineers, educators and students about the latest discoveries and realizations in the foundations, theory, models and applications of systems inspired on nature, using computational intelligence methodologies, as well as in emerging areas related to the three tracks of the conference: Communication Engineering, Knowledge, and Information Technology. The best 25 papers to be included in the book will be carefully reviewed and selected from numerous submissions, then revised and expanded to provide deeper insight into trends shaping future ICT.

Section I

Information Technology

1

Computer Vision for Object Recognition and Tracking Based on Raspberry Pi

Ali A. Abed and Sara A. Rahman

Contents

1.1Introduction

1.2Object Tracking Using CamShift Algorithm

1.2.1Mean-Shift Algorithm

1.2.2CamShift Algorithm

1.2.3Account for Search Window Size

1.3Object Tracking Using Color Detection

1.4Histogram Equalization

1.5System Requirement

1.5.1Raspberry Pi

1.5.2Camera Module

1.6Results

1.7Conclusion and Future Works

References

1.1Introduction

Vision-based systems have become part of everyday life; therefore this work will be in the field of artificial vision based on image processing suitable for many applications such as mobile robots navigation. Computer vision is a type of processing that inputs images producing output that could be a set of characteristics or parameters related to images. Its application in robotics, surveillance, monitoring, and security systems makes it very important and widespread worldwide. The work starts with creating a model to make useful decisions about real physical objects and scenes. A camera mimics and uses real-time digital videos for object recognition and tracking [1].

Object tracking is the main task in the field of computer vision. It has many applications in traffic control, human–computer interaction, digital forensics, gesture recognition, augmented reality, and visual surveillance [2]. An efficient tracking algorithm will lead to the best performance of higher-level vision tasks, such as automated monitors and human–computer interaction. Among the various tracking and recognition algorithms, CamShift tracking and color detection algorithms will be adopted in this chapter. CamShift is primarily intended to perform efficient head and face tracking in a perceptual user interface. It is based on an adaptation of mean shift that, given a probability density image, finds the mean (mode) of the distribution by iterating in the direction of maximum increase in probability density [3]. The aim of the color detection is to identify the category pixel color in a given image. Color detection had already gained the attention of researchers for the possibility of robust and efficient human body detection.

Zhao et al. [4] proposed a method for tracking objects with their size and shapes that change with time, on the basis of a group of mean-shift and affine structure. The results showed the object’s tracking capability in during scale change and partial blockage. Emami [5] submitted an effective color-based CamShift algorithm for target tracking.

Altun et al. [6] suggested a method for efficient color detection in RGB space in hierarchical structure of neural networks. The results show that the proposed hierarchical structure of neural networks is best on traditional neural network classifier in color detection. Zhang et al. [7] used the color cooccurrence histogram (CH) for recognizing objects in images. The results show that the algorithm works in spite of confusing background clutter and moderate amounts of blockage and object praise. Although the color detection and CamShift algorithms have many advantages, they do not work in the dark and this makes it difficult to continue tracking during the lack of light, so in this chapter, we introduce an algorithm that makes these algorithms operate normally even in darkness.

In this chapter, a new method for recognition and tracking by using CamShift and color detection is proposed and compared. All the presented algorithms were programmed with Python programming language supported by OpenCV libraries, and executed with a credit card–size computer board called Raspberry Pi with attached external camera.

1.2Object Tracking Using CamShift Algorithm

1.2.1Mean-Shift Algorithm

Mean shift is a nonparametric feature-space analysis technique for locating the maxima of a density function. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining. The mean-shift tracker provides accurate identification of the location and it is computationally possible. The form that is used widespread for target representation is color histograms, due to its independence from scaling and for the purposes of rotation and its durability to partial blockage. To keep track of the target using the mean-shift algorithm, it repeats the following steps [7]:

1.Choose a search window size and the initial site of the search window.

2.Account for the mean site in the search window.

3.Determine the center of the search window at the mean site computed in step 2.

4.Repeat steps 2 and 3 until rapprochement (or until the mean location moves less than a predefined threshold).

1.2.2CamShift Algorithm

CamShift refers to the continuously adaptive mean shift algorithm. A probabi...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Preface
• Editors
• Contributors
• Section I: Information Technology
• Section II: Communication Systems
• Section III: Management
• Index