PROBLEM FORMULATION AND RESEARCH METHODOLOGY

Transcription

1 PROBLEM FORMULATION AND RESEARCH METHODOLOGY

2 ON THE SOFT COMPUTING BASED APPROACHES FOR OBJECT DETECTION AND TRACKING IN VIDEOS

3 CHAPTER 3 PROBLEM FORMULATION AND RESEARCH METHODOLOGY The foregoing chapter has elaborated on progress, growth and development of various conventional and soft computing based techniques for object detection and tracking in videos. The chapter described peculiarities of these techniques and how soft computing techniques had benefitted the deployment of sensitive applications. The chapter also listed number of analysis and inferences drawn as a result of progress made by such approaches. The presence of large number of issues deems it necessary to analyze these studies and narrow down our focus to available gaps in these studies. The recapitulation of gaps is an important aspect as these act as a platform for current innovations and further research in this field. The present chapter helps us to formulate object detection and tracking problem and discuss an effective research methodology to solve it. 3.1 RESEARCH GAPS Work done in the field of object detection and tracking using soft computing techniques is a comprehensive compilation of progress made by such techniques. The studies explored in previous chapter justifies that soft computing techniques can still play an important role in devising promising solutions for detection and tracking. It is still on open domain for researchers for further improvements. After critical analysis of studies done over recent past and understanding requirements of various applications, a number of areas have been listed where researchers can focus to carry out further investigations. It can be observed that illumination changes along with handling of dynamic background is a widely targeted research challenge and finds implementation among all major soft computing approaches. Handling of noise and occlusions in videos is also sought after research challenge for which fuzzy logic and evolutionary based algorithms have been used extensively. There has been little progress in handling of camera jitter and shadow detection methods although some studies had tried to provide solutions for these areas but it still remains a major research challenge. 89

4 ON THE SOFT COMPUTING BASED APPROACHES FOR OBJECT DETECTION AND TRACKING IN VIDEOS Another direction towards efficient object detection and tracking is to develop methods that can handle background clutter, occlusions, dynamic background or noisy images etc. There had been some attempts with advent of deep learning based convolutional neural networks but it is still an open issue for researchers. Optimization approaches for object detection and tracking objects in videos have been proposed aiming to improve classification of pixels for e.g. Particle Swarm Optimization has been implemented for tracking multiple objects. The success rate of these applications have been found to be better than standard tracking algorithms but development of a generic model to track objects with different shapes and sizes is still far from reality. Hybrid methods of object detection and tracking approaches in videos focus on combining the best properties of two or more object detection and tracking techniques applied in videos. Recent initiatives such as [148],[149],[150] [151],[154] etc. show that researchers take interest in applying hybrid techniques to achieve accurate results. They have been successful in addressing issue related to uncertainty in detection process caused by the cited background maintenance phase of background subtraction approach. The experimental results on real color video sequence showed promising results and redirected researcher s attention towards hybrid approaches of object detection in videos. Techniques on neuro-fuzzy methods have been frontrunner in hybridization where as an attempt shall be made to combine other evolutionary approaches also. Threshold plays a very critical role in effective classification of pixels as foreground or background and can govern accuracy and preciseness of object boundaries in any detection and tracking algorithm. Suitable threshold detection algorithm aimed at providing an optimal value for variety of videos can be proposed and developed using suitable soft computing based method. Effective modeling of background from contents of previous frame is also an open issue. It has been found in previous research that decision regarding frequency and rate of update could also influence results of detections therefore it can also be an area for future research. 90

5 CHAPTER 3 PROBLEM FORMULATION AND RESEARCH METHODOLOGY Entropy based methods help to make detections on the basis of video content and its combination with standard background subtraction method can be helpful in devising effective detection method. One such method has been explored by researchers in [125] but still the number of studies is very limited. This area can also be targeted together with soft computing technique for optimization because of their effective exploitation within search space and capabilities to avoid local minima and maxima. One of the major issues with all soft computing approaches is the time taken to converge to a global optimum solution; no such study is available in which comparisons with respect to settling time have been made with two or more soft computing based algorithms. Some new and very fast soft computing techniques have been proposed recently like Ions Motion Algorithm [179], Water Cycle Algorithm [180], Water wave Optimization [181], Mine Blast Optimization [182] that shows quick convergence time as compared to techniques like genetic algorithm, PSO etc. Solutions based on such techniques can be explored for real time video analysis and processing. Preprocessing of videos is also one of the emergent areas that can improve the detection results. Relatively small number of studies using soft computing techniques had been proposed for preprocessing or enhancement of videos. Studies based on nature inspired algorithms available at [183], [184] could be explored for such research directions. All issues listed above require a deep exploration of concepts from computer vision and optimization theory. Authors in current study have targeted an area in which few explorations have been made. The area that stands out from rest is application of soft computing techniques together with the use of entropy based principles for object detections and tracking. There have been very limited exploitation of these concepts and one such similar study is available at [125]. The cited study has used concepts of image entropy to propose a solution on the basis of p-median problem. The present study uses concepts of fuzzy 2-partitiotn entropy and recently proposed optimization theories to provide better solution as compared to previously available solutions. In addition, an effective enhancement study based on a recent soft computing technique is also targeted. The enhancement method is used for pre-processing of input frames before making detections. 91

6 ON THE SOFT COMPUTING BASED APPROACHES FOR OBJECT DETECTION AND TRACKING IN VIDEOS 3.2 PROBLEM FORMULATION Background subtraction approach is used to detect moving objects in scene by comparing every frame to a background model. It has been found that use of conventional background subtraction approaches fail to work well with issues such as noise, dynamic environment and complex videos. The limitations of these methods have led researchers to shift their focus towards soft computing approaches for development of effective and optimized models. Soft computing is a field in computer science that uses inexact solutions for computationally hard problems. There is enough literature to signify that object detection and tracking is computationally hard problem for which a large number of soft computing approaches had been implemented. There have been considerable studies by different authors who had applied soft computing approaches like artificial neural networks, fuzzy logic, genetic algorithms, particle optimization, deep learning etc in this regard. These theories have been utilized at various steps in different algorithms for e.g. neural network based object detection and tracking approaches have been dominant in proposing effective classification methods because of their underlying working principles, optimization theories have been effectively implemented for tracking, or modeling of background etc. Approaches such as Genetic Algorithm Fuzzy, Neural network provides better results than conventional approaches but still these methods are very complex and complicated. There are many interference factors such as occlusion, shadows, camera shake problem etc. that hamper effective detection process. Available soft computing techniques shall be studied for their suitable applicability and tested for analysis. The selected technique shall be used for effectively classifying pixels into classes of background and foreground while trying to reduce effect of issues such as noise, illumination changes, etc. A major issue in using these approaches is their applicability in low quality videos and effective results in real time. It has been found that previously used algorithms fail on these prospective. The proposed research in this study is targeted at requirements discussed earlier and following are main points of action. Object detection and tracking technique is developed focusing on performance optimization of one of the most frequently used detection method in video processing namely, Background Subtraction (BS). Background subtraction is 92

7 CHAPTER 3 PROBLEM FORMULATION AND RESEARCH METHODOLOGY chosen because of its simple mechanism and wider acceptance and can be implemented on any platform or hardware. The standard BS method is enhanced by using concept of fuzzy 2-partition entropy and Big Bang Big Crunch Optimization (BBBCO). A new variant of BS algorithm using Big Bang Big Crunch Optimization and entropy based detection is proposed and implemented. BBBCO is a recently proposed evolutionary optimization approach for providing solutions to problems operating on multiple variables within prescribed constraints. BBBCO is used for extracting various parameters by framing the problem of threshold detection as optimization problem which is solved using concept of fuzzy partition entropy. BBBCO based background subtraction algorithm has helped to make the technique applicable to real life videos and environments by providing effective modeling of background from current frame. This is done by extracting optimal values for threshold, rate of updation of background and other parameters according to the contents of the frame using proposed algorithm. A tracker then tracks trajectories of detected object. In order to improve detection results, pre-processing of input video is always desired as it helps in enhancement of edges and contrast eventually leading to good classification of pixels. Keeping this point in mind, a new contrast enhancement method based on water cycle based optimization (WCA) method is proposed. Optimization based enhancement technique is proposed so as to make it applicable to different datasets with variety of videos. Implementation of enhancement method with WCA has helped to control degree of enhancement required for different videos and has also helped to preserve the inherent details of original frame. WCA based enhancement implements a multi-objective optimization function so that over-enhancement and truncation of some pixel values could be negated. The technique is also compared with other contemporary approaches in the proposed field. The proposed method is evaluated on videos from benchmark datasets both quantitatively and qualitatively. The performance is presented in terms of visual comparison along with a number of statistical methods. Suitable comparisons have 93

8 ON THE SOFT COMPUTING BASED APPROACHES FOR OBJECT DETECTION AND TRACKING IN VIDEOS been included with state of art practices in relevant field to justify effectiveness of approach. 3.3 RESEARCH DESIGN Research design is a strategy, a plan and a structure for conducting research. It is comprehensive master plan of research study to be undertaken, giving a general statement of methods to be used. Selection of research design is to ensure that requisite data is in accordance with the problem at hand and is collected accurately and economically. It is frame work or blueprint for research study that guides collection and analysis of data. Research design depending upon needs of researcher may be a detailed statement or only furnishing of minimum information required for planning research project. Every design has its positive and negative sides and best design depends upon research question as well as orientation of researcher. Various modes of research design are: Theoretical research design, applied research design, exploratory design, descriptive design, quasi-experimental design and experimental design. Present research is based on exploratory, experimental and applied research design. It is exploratory as it consists of discovery of new ideas and possible insights in identifying areas of further study. It is experimental as data discovery of new findings is supported by conducting experiments. Evaluations are done on MATLAB which is accepted scientifically among research peers and these findings can be verified also. The research problem undertaken in this study is industry specific therefore it falls under the category of applied research design. Data for sake of research study has been taken from benchmark datasets which are made available by fellow researchers for benefit of research community. The simulator used is developed by an independent international proprietary organization which is certified and widely used in research, security by government organizations across the world. The authenticity of work is also supported by comparing designed work with standard approaches. The available studies in field of object detection and tracking had been analyzed and their detailed analysis has led to decision of designing an approach based on entropy concepts. The proposed technique is automatic, adaptive and applicable in real world environment. Designing such a system is only possible if information from video frames is used to motivate decisions regarding detection of foreground and 94

9 CHAPTER 3 PROBLEM FORMULATION AND RESEARCH METHODOLOGY background. Keeping this point in mind, concept of image entropy was explored and fuzzy 2-partition method is chosen as base for separation of foreground and background. Since an adaptive approach was desired, therefore authors were inclined to powers of soft computing techniques and decided to automate this process with help of big bang big crunch optimization (BBBCO) technique. BBBCO is chosen since it is one of latest evolutionary algorithm that can overcome local minima and can seek convergence quickly. A new background subtraction algorithm implemented with help of BBBCO has been proposed. The algorithm also proposes suitable method to update the background effectively based on contents of frame. The proposed technique is tested on benchmark datasets and compared with another entropy based approach available in literature. In order to make the proposed approach suitable to be deployed on complex and hazy videos, preprocessing of videos is a desired operation. The authors in this present study have proposed a new contrast enhancement operation for videos. An enhancement technique is implemented with help of water cycle algorithm (WCA) that derives its inspiration from nature s water cycle. The technique uses a multiobjective function to enhance contrast of videos and makes suitable adjustments to preserve colorfulness and details of input frame. The technique is tested on hazy images and video sequences downloaded from benchmark datasets. The results are compared with number of contemporary techniques in terms of number of statistical parameters. The proposed algorithms are implemented in MATLAB R2015b on a standard computing machine. The proposed enhancement technique is tested on normal, natural and hazy videos. The obtained results are compared with contemporary approaches in the present field. The comparisons are made in terms of visual results and statistical parameters. The next phase of object detection and tracking implements a modified version of background subtraction. BBBCO-BS algorithm tries to extract an optimal value of threshold for the frame in question and helps to model the background also. The detections made by the algorithms are inputted to Kalman filter based multiple object tracker. The proposed algorithm is also implemented in MATLAB. The approach is validated by comparing with standard practices in the field and in addition, it is also put to competition against recent approach available in 95

10 ON THE SOFT COMPUTING BASED APPROACHES FOR OBJECT DETECTION AND TRACKING IN VIDEOS literature. The obtained results are tabulated and made available for future explorations. 3.4 LIMITATIONS OF STUDY The work proposed in this study is novel contribution made by authors to develop detection and tracking scheme for moving objects. The study has been conducted with the following limitations or assumptions 1. The study has been conducted on benchmark datasets and results have been reported in accordance to the truth values available in datasets. 2. The considered videos are above a minimum frame rate with presence of moving objects. 3. No restrictions on shape and size of objects have been imposed. 4. The study has been simulated therefore calibrations made by simulator for calculations may be considered for developing hardware solutions. 5. The research articles arising out of present work presents best obtained results. 3.5 CHAPTER SUMMARY This chapter has listed out number of gaps which have been left uncultivated by available studies on object detection and tracking. Presence of these gaps is an indication about multi dimensionality of the problem at hand. The analysis of gaps has enabled us to narrow down detection problem to solve it with help of big bang big crunch optimization and fuzzy entropy. Formulation of problem and its statement is provided in this chapter. The problem is to be solved by designing detector and tracker algorithm supported by an enhancement method. The working model and research methodology adopted for design of this framework is also discussed. Every study is completed within certain limitations and chapter ends by listing these limitations. 96

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