Design and implementation of Camera Based Object Tracking in 3D Space

Object tracking is the task of capturing the 3D position and pose of an object from frame to frame.In this paper we have presented an application based on gesture to control robotic arm through human arm.This arm is based on ATmega16.It is a servo motor based robotic arm with multiple degrees of freedom and having capability to rotate at maximum point in region.This microcontroller based servo motor controller will be controlled through computer system in order to control the individual motor on the basis of provided angle. The Vb.net designed software processing approach has created human computer interaction in order to control hardware baesd robotic arm in 3D co-ordinates

3D Object Class Detection in the Wild

Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations. Only recently, there has been a growing interest in revisiting the promise of computer vision from the early days: to precisely delineate the contents of a visual scene, object by object, in 3D. In this paper, we draw from recent advances in object detection and 2D-3D object lifting in order to design an object class detector that is particularly tailored towards 3D object class detection. Our 3D object class detection method consists of several stages gradually enriching the object detection output with object viewpoint, keypoints and 3D shape estimates. Following careful design, in each stage it constantly improves the performance and achieves state-ofthe-art performance in simultaneous 2D bounding box and viewpoint estimation on the challenging Pascal3D+ dataset

A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images

We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object. Our method relies only on grayscale images since depth cameras fail on metallic objects; it can handle poorly textured objects, and cluttered, changing environments; the pose it predicts degrades gracefully in presence of large occlusions. As a result, by contrast with the state-of-the-art, our method is suitable for practical Augmented Reality applications even in industrial environments. To be robust to occlusions, we first learn to detect some parts of the target object. Our key idea is to then predict the 3D pose of each part in the form of the 2D projections of a few control points. The advantages of this representation is three-fold: We can predict the 3D pose of the object even when only one part is visible; when several parts are visible, we can combine them easily to compute a better pose of the object; the 3D pose we obtain is usually very accurate, even when only few parts are visible

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing

Simultaneous Association and Localization for Multi-Camera Multi-Target Tracking

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 최진영.In this dissertation, we propose two approaches for three-dimensional (3D) localizing and tracking of multiple targets by using images from multiple cameras with overlapping views. The main challenge is to solve the 3D position estimation problem and the trajectory assignment problem simultaneously. However, most of the existing methods solve these problems independently. Unlike single camera multi-target tracking, it is much more complicated to solve both problems because the relationship between cameras is also taken into consideration in multi-camera. To tackle this challenge, we present two approaches: mixed multidimensional assignment approach and variational inference approach. In the mixed multidimensional assignment approach, we formulate the data association and 3D trajectory estimation problem as the mixed optimization problem with discrete and continuous variables and suggest an alternative optimization scheme which jointly solves the two coupled problems. To handle a large solution space, we develop an efficient optimization scheme that alternates between two coupled problems with a reasonable computational load. In this optimization formulation, we design a new cost function that describes 3D physical properties of each target. In the variational inference approach, we establish a maximum a posteriori (MAP) problem over trajectory assignments and 3D positions for given detections from multiple cameras. To find a solution, we develop an expectation-maximization scheme, where the probability distributions are designed by following the Boltzmann distribution of seven terms induced from multi-camera tracking settings.1 Introduction 1 1.1 Background & Challenges 1 1.2 Related Works 4 1.3 Problem Statements & Contributions 8 2 Mixed Multidimensional Assignment Approach 12 2.1 Problem Formulation 12 2.1.1 Problem Statements 12 2.1.2 Cost Design 17 2.2 Optimization 22 2.2.1 Spatio-temporal Data Association 23 2.2.2 3D Trajectory Estimation 31 2.2.3 Initialization 33 2.3 Application: Real-time 3D localizing and tracking system 35 2.3.1 System overview 36 2.3.2 Detection 37 2.3.3 Tracking 39 2.4 Appendix 42 2.4.1 Derivation of equation (2.35) 42 3 Variational Inference Approach 44 3.1 Problem Formulation 44 3.1.1 Notations 44 3.1.2 MAP formulation 46 3.2 Optimization 48 3.2.1 Posterior distribution 48 3.2.2 V-EM algorithm 51 3.3 Appendix 56 3.3.1 Derivation of equation (3.12) 56 3.3.2 Derivation of equation (3.27-3.32) 56 3.3.3 Deriving optimal mean and covariance matrix (3.33-3.35) 59 3.3.4 Definition of A and b in (3.22) 62 4 Experiments 63 4.1 Datasets 63 4.1.1 PETS 2009 63 4.1.2 PSN-University 64 4.2 Evaluation Metrics 66 4.3 Results and Discussion 67 4.3.1 Mixed Multidimensional Assignment Approach 67 4.3.2 Variational Inference Approach 82 4.3.3 Comparisons of Two Approaches 93 5 Conclusion 98 5.1 Concluding Remarks 98 5.2 Future Work 99 Abstract (In Korean) 112Docto