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Performance Evaluation Software

Moving Object Detection and Tracking in Videos

Authors: Bahadir Karasulu, Serdar Korukoglu

Publisher: Springer New York

Book Series : SpringerBriefs in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

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About this book

Performance Evaluation Software: Moving Object Detection and Tracking in Videos introduces a software approach for the real-time evaluation and performance comparison of the methods specializing in moving object detection and/or tracking (D&T) in video processing. Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic algorithms for video object D&T in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean-shift (CMS), Optical Flow (OF), etc. An important problem for performance evaluation is the absence of any stable and flexible software for comparison of different algorithms. In this frame, we have designed and implemented the software for comparing and evaluating the well-known video object D&T algorithms on the same platform. This software is able to compare them with the same metrics in real-time and on the same platform. It also works as an automatic and/or semi-automatic test environment in real-time, which uses the image and video processing essentials, e.g. morphological operations and filters, and ground-truth (GT) XML data files, charting/plotting capabilities, etc. Along with the comprehensive literature survey of the abovementioned video object D&T algorithms, this book also covers the technical details of our performance benchmark software as well as a case study on people D&T for the functionality of the software.

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Table of Contents

Frontmatter

Chapter 1. Introduction

Abstract

This chapter comprises three sections. The first section represents the scope of this book. The second section introduces the related works on the issue of moving object detection and tracking (D&T) in videos. Also, the main objective for moving object D&T and main scenarios for visual surveillance applications is given in this chapter. The moving object D&T methods in video processing are categorized in some ways where their respective aspects are taken as the basis of the D&T process. In the second section, the basis of performance and evaluation of moving object D&T process is declared, such as with-ground-truth and without-ground-truth evaluation. In addition, commonly used video datasets, their tools, and some systems for object D&T are briefly introduced. This chapter ends with a third section focused on the main contribution of the study given in this book.

Bahadir Karasulu, Serdar Korukoglu

Chapter 2. Moving Object Detection and Tracking in Videos

Abstract

This chapter provides four sections. The first section introduces the moving object D&T infrastructure and basis of some methods for object detection and tracking (D&T) in videos. In object D&T applications, there is manual or automatic D&T process. Also, the image features, such as color, shape, texture, contours, and motion can be used to track the moving object(s) in videos. The detailed information for moving object detection and well-known trackers are presented in this section as well. In second section, the background subtraction (BS) method and its applications are given in details. The third section declares the details for Mean-shift (MS), Mean-shift filtering (MSF), and continuously adaptive Mean-shift (CMS or CAMShift) methods and their applications. In fourth section, the details for the optical flow (OF), the corner detection through feature points, and OF-based trackers are given in details.

Bahadir Karasulu, Serdar Korukoglu

Chapter 3. A Software Approach to Performance Evaluation

Abstract

This chapter provides four sections. The first section introduces the ViCamPEv software infrastructure (i.e., OpenCV, etc.), the CAVIAR video dataset, and its CVML (i.e., XML GT file format), and some other studies in the literature (e.g., MERL-PEP platform). In the second section, the architectural overview of the ViCamPEv software is presented and how the GT data is used to measure and evaluate the performance of given object D&T methods. Also, UML-based component diagram of software and the development platform of software are presented. In the third section, the testing conditions and testbed (i.e., software and hardware) are explained, and detailed via the screenshots of ViCamPEv’s GUI windows. In the fourth section, the system workflow is given via two parts of ViCamPEv: the Camera part and the Video part. The system is treated as automatic or semi-automatic test environment, which combines the input data with parameters of given methods.

Bahadir Karasulu, Serdar Korukoglu

Chapter 4. Performance Measures and Evaluation

Abstract

This chapter provides two sections. The first section introduces our performance evaluation methodology. The second section is separated into three subsections, which are frame-based detection measures, measures based on object matching criteria, and object tracking-based measures, respectively. In our study, some performance measures are used to evaluate object D&T method’s performance. In this manner, the measures for frame-based detection are object count accuracy, pixel-based precision, pixel-based recall, pixel-based F1 measure, area-based precision, area-based recall, area-thresholded precision, area-thresholded recall, and average fragmentation, respectively. Also, the measures based on object matching criteria are sequence frame detection accuracy, and position-based, size-based, and orientation-based measures, respectively. In addition, the measures based on object tracking are the object count-based measure, temporal measure, and sequence tracking detection accuracy, respectively. All of the above-mentioned measures are given via mathematical background in related subsections of this book.

Bahadir Karasulu, Serdar Korukoglu

Chapter 5. A Case Study: People Detection and Tracking in Videos

Abstract

This chapter provides three sections. The first section introduces the video database that it is used in our experiments. This database is based on people surveillance footage and called CAVIAR. In our study, the performance results are based on both qualitative and quantitative evaluation. In second section, some experimental results of our study are presented via qualitative and overall quantitative (i.e., numerical) results of performance. In addition, we explain that the capabilities of ViCamPEv are used to obtain these results. In third section, we present both statistical and algorithmic analysis of relevant object D&T methods. These methods are comparable via the results in related tables for related methods. The discussion about given methods are presented through these experimental results and performance evaluation.

Bahadir Karasulu, Serdar Korukoglu

Chapter 6. Conclusion

Abstract

This chapter concludes the book, also it involves a summary of the study. Therefore, it declares some contributions of given study. In addition, this chapter suggests to readers or researchers in computer vision and multimedia research area that the ViCamPEv software is useful for image and video processing, multimedia content, and information retrieval as well.

Bahadir Karasulu, Serdar Korukoglu

Backmatter

Title: Performance Evaluation Software
Authors: Bahadir Karasulu
Serdar Korukoglu
Copyright Year: 2013
Publisher: Springer New York
Electronic ISBN: 978-1-4614-6534-8
Print ISBN: 978-1-4614-6533-1
DOI: https://doi.org/10.1007/978-1-4614-6534-8