Real Time Tracking and Identification Of Moving Persons By Using A Camera In Outdoor Environment

A new method for detecting and tracking of moving persons based on low resolution image employing peripheral increment sign correlation image and identifying the moving persons by their color and spatial information is proposed in this paper. Many tracking algorithms have better performance under a static background in indoor environ-ment. It is, however, most of the tracking algorithms are applied in outdoor environment with noisy background instead of indoor environment. Since a low resolution image has a property that it can remove the small size pixels, it is adopted to solve the problem of the noisy background. In the tracking of a target object, many applications have problem when object occlude each other. A block matching technique based on peripheral incre-ment sign correlation image is utilized to solve this problem. The identi_cation of a target object is performed using color and spatial information of the target object. The experimental results prove the feasibility and usefulness of the proposed metho

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Static hand gesture segmentation for images with complex background; detection and tracking of dynamic hand gesture

This thesis presents color hand gesture segmentation for static images with complex background along with tracking and detection of hand gesture from video sequence. This thesis consists of two works: 1) Static. 2) Dynamic. In the first part, aim is to automatically segment the hand gesture from a given image under different luminance conditions and complex backgrounds. The luminance value affects the color component of an image which leads to increase the noise level in the segmented image. This paper proposes a combined model of two color spaces i.e., HSI, YCbCr and morphological operations with labeling to improve the segmentation performance of color hand gesture from complex backgrounds in terms of completeness and correctness. The proposed color model separates the chrominance and luminance components of the image. The performance of the proposed method is demonstrated through simulation and the experime ntal results reveal that proposed method provides better performance accuracy compared to the HSI and YCbCr methods individually in terms of correctness and completeness. In the second part, aim is to automatic detection and tracking of hand gesture from v ideo sequence under different backgrounds. It involves three steps: 1). Hand tracking 2). Hand detection 3). Hand identification.Here all the simulations are done in MATLAB 10 environment

A survey on object detection and tracking algorithms

Object detection and tracking are important and challenging task in many computer vision applications such as surveillance, vehicle navigation and autonomous robot navigation. Video surveillance in dynamic environment, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of traffic. The work involves designing of efficient video surveillance system in complex environments. In video surveillance, detection of moving objects from a video is important for object detection, target tracking, and behaviour understanding. Detection of moving objects in video streams is the first relevant step of information and background subtraction is a very popular approach for foreground segmentation. In this thesis, we have simulated different background subtraction methods to overcome the problem of illumination variation, background clutter and shadows. Detecting and tracking of human body parts is important in understanding human activities. Intelligent and automated security surveillance systems have become an active research area in recent time due to an increasing demand for such systems in public areas such as airports, underground stations and mass events. In this context, tracking of stationary foreground regions is one of the most critical requirements for surveillance systems based on the tracking of abandoned or stolen objects or parked vehicles

Hand detection and segmentation using smart path tracking fingers as features and expert system classifier

Nowadays, hand gesture recognition (HGR) is getting popular due to several applications such as remote based control using a hand, and security for access control. One of the major problems of HGR is the accuracy lacking hand detection and segmentation. In this paper, a new algorithm of hand detection will be presented, which works by tracking fingers smartly based on the planned path. The tracking operation is accomplished by assuming a point at the top middle of the image containing the object then this point slides few pixels down to be a reference point then branching into two slopes: left and right. On these slopes, fingers will be scanned to extract flip-numbers, which are considered as features to be classified accordingly by utilizing the expert system. Experiments were conducted using 100 images for 10-individual containing hand inside a cluttered background by using Dataset of Leap Motion and Microsoft Kinect hand acquisitions. The recorded accuracy is depended on the complexity of the Flip-Number setting, which is achieved 96%, 84% and 81% in case 6, 7 and 8 Flip_Numbers respectively, in which this result reflects a high level of finite accuracy in comparing with existing techniques

Object Tracking Using Adaptive Frame Differecing and Dynmaic Template Matching Method

In this project I have used the concept of frame differencing and background subtraction algorithm to propose a modified algorithm which can be used effectively and accurately with comparison to both frame differencing method and background subtraction model used individually for detecting moving objects in a sequence of frames. In this project we have used the method of frame differencing to propose a new adaptive frame differencing method which shall take account of the velocity of the moving object in order to find the number of frames to be skipped each stage of detection to calculate inter-frame difference in order to get the region of moving object. The above procedure is combined with background subtraction model with a new idea of changing the background dynamically to have a better image of the moving object. The area obtained from adaptive frame differencing is added with the area obtained from adaptive background subtraction model to have a clear view of the pixels associated with the moving object. After getting the detected object the centroid of it is passed to the tracking module in order to track the object in upcoming frames by using the concept of dynamic template matching algorithm which uses a correlation function in order to track the detected object in the region of interest in the upcoming frames. When the tracking fails the algorithm goes back to detection module and the process repeats. Thus we proposed a effective tracking algorithm which can be use even if the object of interest is far away from the camera independent of the motion of the object

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention