Aktif kontur modeli ve sezgisel yöntem ile dijital görüntüde nesne bölümleme ve izleme

In this thesis, object segmentation and tracking in digital image with Active Contour model and heuristic method and along with it multiple ground target tracking algorithms are studied. From different aspects of the ground target tracking, three different types of tracking algorithms are proposed according to the specialties of the ground target motion and sensors employed. Firstly, the dependent target tracking for ground targets is studied. State dependency is a common assumption in traditional target tracking algorithms, while this may not be the true in ground target tracking as the motion of targets are constraint to certain path. This approach largely reduces the exhaustive searching in common state-of-art trackers while maintains efficient representation of the target appearance change. The primary contribution of this project will be a method to propose a simple approach to take advantage of state-of-the-art methods to apply to video object segmentation problem using active contours with heuristic methods for reappearance detection. Experiments on 100 public benchmark videos, as well as a high frame rate benchmark, are carried out to compare the performance with the state-of-art published algorithms. The results of the experiment show the proposed tracker achieves good performance while beats other algorithms in speed with a large margin. The proposed visual target tracker is integrated into a new multiple ground target tracking algorithm using a single camera. The multi-target tracker addresses the issues in the target detection, data association and track management aside from the single target tracker. A perspective aware detection algorithm utilizing the recent advanced Convolutional Neural Networks (CNN) based detector is proposed to detect multiple ground targets and alleviate the weakness of CNN detectors in detecting small objects. A hierarchical class tree based multi-class data association is presented to solve the multi-class association problem with potential misclassified detections. Track management is also improved utilizing the high efficiency detectors and a Support Vector Machine (SVM) based track deletion is proposed to correctly remove the dead tracks. Benchmarking is presented in experiments and results are analyzed. A case study of applying the proposed algorithm is provided demonstrating the usefulness in real applications.