A Novel Approach for Image Localization Using SVM Classifier and PSO Algorithm for Vehicle Tracking

In this paper, we propose a novel methodology for vehicular image localization, by incorporating the surveillance image object identification, using a local gradient model, and vehicle localization using the time of action. The aerial images of different traffic densities are obtained using the Histograms of Oriented Gradients (HOG) Descriptor. These features are acquired simply based on locations, angles, positions, and height of cameras set on the junction board. The localization of vehicular image is obtained based on the different times of action of the vehicles under consideration. Support Vector Machines (SVM) classifier, as well as Particle Swarm Optimization (PSO), is also proposed in this work. Different experimental analyses are also performed to calculate the efficiency of optimization methods in the new proposed system. Outcomes from experimentations reveal the effectiveness of the classification precision, recall, and F measure

DETC2005-85314 AN APPROACH TO DRAWING-LIKE VIEW GENERATION FROM 3D MODELS

ABSTRACT In this paper we propose a method to generate 2D drawinglike views from 3D models automatically. The view generation process is conducted in object space and supported by two algorithms: (1) pose determination for 3D models: unifying the space between 2D drawings and 3D models; and (2) 2D drawing-like view generation from 3D models: building the correspondence between 2D drawings and 3D models. The pose determination method for 3D objects is proposed on the basis of a concept called Virtual Contact Area. Meanwhile an efficient occlusion algorithm based regular grid is described to generate orthogonal drawing-like views from 3D models along the pose orientations. To evaluate the validity of the proposed methods, respective experiments are presented. INTRODUCTION As two different ways to express and communicate design ideas, 2D drawings and 3D models are now being widely used in many fields. For designers with special skills, 2D drawings are usually used as the principal way to express ideas; while for most common users, 3D models are more intuitive than 2D drawings from the perspective of information communication. How to seamlessly transit between the two representations is a public problem for many researchers in engineering fields. In spite of the fact that many methods To find the optimal orientations for a 3D model, Park et al. [4] used a pose determination technique to integrate tw

Pose evaluation based on bayesian classification error

Pose evaluation is a fundamental issue in image processing and computer vision. In this paper, we propose a new method called BCE for pose evaluation based on Bayesian classification error. Various image cues are incorporated to depict an object including object shape, side region statistics and temporal information. Then a PEF (Pose Evaluation Function) is constructed based on Bayesian classification error, and an efficient algorithm to calculate it is developed. We test our new method with real outdoor image sequences, and use two criteria to compare it with two other representative ones. It is shown that our new method leads to better performance with respect to localization accuracy and robustness against general clutter and occlusion.