Enhanced Tracking Aerial Image by Applying Frame Extraction Technique

An image registration method is introduced that is capable of registering images from different views of a 3-D scene in the presence of occlusion. The proposed method is capable of withstanding considerable occlusion and homogeneous areas in images. The only requirement of the method is for the ground to be locally flat and sufficient ground cover be visible in the frames being registered. With help of fusion technique we solve the problem of blur images. In previous project sometime object recognition is not possible they do not show appropriate area, path and location. So with the help of object recognition we show the appropriate location, path and area. Then it captured the motion images, static images, video and CCTV footage also. Because of occlusion sometime result not get correct or sometime problems are occurred but with the help of techniques solve the problem of occlusion. This method is applicable for the various investigation departments. For the purpose of tracking such as smuggling or any unwanted operations which are apply or performed by illegally. Various types of technique are applied for performing the tracking operation. That technique return the correct result according to object tracking. Camera is not supported this type of operation because they do not return the clear image result. So apply the drone and aircraft for capturing the long distance or multiview images

A Comprehensive Review on Computer Vision Analysis of Aerial Data

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.Comment: 112 page