Bahadir Karasulu

Performance evaluation software

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.

Moving Object Detection and Tracking in Videos

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.

An approach based on simulated annealing to optimize the performance of extraction of the flower region using mean-shift segmentation

Flower identification and recognition are tedious and difficult tasks even for humans. Image segmentation based on automatic flower extraction is an essential step for computer-aided flower image recognition and retrieval processes. Furthermore, there is a challenge for segmentation of the object(s) from natural complex background in color images. In this study, a novel performance optimization approach for image segmentation, i.e. simulated annealing-based mean-shift segmentation (SAMS), is proposed and implemented. It is based on the simulated annealing solution of quadratic assignment problem model treated as an image segmentation process using feature-based mean-shift (MS) clustering on color images. The proposed approach is designed to realize a global and unsupervised (i.e., fully automatic) segmentation. It is a modified and optimized version of Backprojection-based mean-shift segmentation (BackMS) method. In conducted segmentation experiments, the performance results of SAMS approach are compared with the ones of BackMS method. Comparison of overall performance results and statistical analysis (i.e., Wilcoxon signed rank median test) show that SAMS approach improves the performance of BackMS method. It is measured as 49.33% when using object bounding boxes and as 51.33% when using object pixel regions.

A Software Approach to Performance Evaluation

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.

An Optimized Image Segmentation Approach Based on Boltzmann Machine

ABSTRACT Image segmentation with complex background is a tedious task. In our study, a convex spline is constructed based on Good Features to Track (GF2T) method’s region-based salient feature (i.e., corner) set. For an optimized edge-based segmentation, an ellipse shape prior based on this convex spline is useful in edge regularization procedure with region-based features. This kind of optimization is achieved by Boltzmann machine (BM) to automatically form an elliptical foreground mask of the GrabCut method. We demonstrated our approach’s usability through traveling salesman problem (TSP), thus, we consider that the TSP’s valid tour’s path solved by BM can be taken as an optimized convex spline for edge-based segmentation. In our experiments, proposed BM-based approach has the performance improvement of segmentation to stand-alone GF2T as 29.79% improvement based on bounding boxes evaluation and as 38.67% improvement based on the overlapping pixel regions for a quantitative evaluation via objective metrics.

Parallel Computing in Fuzzy Decision Making Systems

Bazı karar problemlerinde, alternatif, kriter ve karar vericilerin sayısı çok yüksektir. Bu yüzden hesaplama işlemi daha zor, zaman alıcı ve karmaşık bir hal alır. Bu karmaşık işlemleri daha kısa sürede gerçekleştirilebilmek için paralel hesaplama gibi popüler teknolojiler mevcuttur. Bu çalışmada, Bulanık Analitik Hiyerarşi Süreci (BAHS) ve TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yöntemlerinden oluşan bulanık bir karar verme sisteminde paralel hesaplamanın tasarlanması ve gerçekleştirimi incelenmiştir. Paralel hesaplama, hesaplamanın en yoğun olduğu BAHS adımında gerçekleştirilmiştir. Önerilen yöntem, homojen ve heterojen bilgisayarlarda ayrı ayrı denenmiş ve sonuçlar tartışılmıştır. In some decision problems, the numbers of alternatives, criteria and decision-makers are very high. Therefore the calculation process becomes more difficult, time consuming and complex. To achieve these complex tasks in a shorter time, popular technologies such as parallel computing are available to use. In this study, design and implementation of parallel computation in a fuzzy decision making system which consists of the methods: Fuzzy Analytic Hierarchy Process (FAHP) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was investigated. Parallel computing was carried out in FAHP phase that is most intensive phase of calculation. Proposed method was tested in homogeneous and heterogeneous computers separately and results were discussed. Anahtar kelimeler: Bulanık karar verme, Paralel hesaplama, Bulanık analitik hiyerarşi süreci, TOPSIS.

Performance Measures and Evaluation

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.

A Case Study: People Detection and Tracking in Videos

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.