<p>In the past decade computer generated images have become widely used in the visual effects industry. One of the main reasons is being able to seamlessly blend three dimensional (3D) animation with live-action footage. In this study, different 3D camera tracking software (also referred to as matchmoving) is compared focusing on workflow, user-friendly system, and quality of production.</p>

In the past decade computer generated images have become widely used in the visual effects industry. One of the main reasons is being able to seamlessly blend three dimensional (3D) animation with live-action footage. In this study, different 3D camera tracking software (also referred to as matchmoving) is compared focusing on workflow, user-friendly system, and quality of production.

The current practice of construction productivity and emission monitoring is performed by either manual stopwatch studies which are significantly labor intensive and subject to human errors, or by the use of RFID and GPS tracking devices which may be costly and impractical. To address these limitations, a novel computer vision based method for automated 2D tracking, 3D localization, and action recognition of construction equipment from different camera viewpoints is presented. In the proposed method, a new algorithm based on Histograms of Oriented Gradients and hue-saturation Colors (HOG+C) is used for 2D tracking of the earthmoving equipment. Once the equipment is detected, using a Direct Linear Transformation followed by a non-linear optimization, their positions are localized in 3D. In order to automatically analyze the performance of these operations, a new algorithm to recognize actions of the equipment is developed. First, a video is represented as a collection of spatio-temporal features by extracting space-time interest points and describing each with a Histogram of Oriented Gradients (HOG). The algorithm automatically learns the distributions of these features by clustering their HOG descriptors. Equipment action categories are then learned using a multi-class binary Support Vector Machine (SVM) classifier. Given a novel video sequence, the proposed method recognizes and localizes equipment actions. The proposed method has been exhaustively tested on 859 videos from earthmoving operations. Experimental results with an average accuracy of 86.33% and 98.33% for excavator and truck action recognition respectively, reflect the promise of the proposed method for automated performance monitoring. / Master of Science

The aim of this thesis is to provide methods for 2D segmentation and 2D/3D tracking, that are both fast and robust to imperfect image information, as caused for example by occlusions, motion blur and cluttered background. We do this by combining high level shape information with simultaneous segmentation and tracking. We base our work on the assumption that the space of possible 2D object shapes can be either generated by projecting down known rigid 3D shapes or learned from 2D shape examples. We minimise the discrimination between statistical foreground and background appearance models with respect to the parameters governing the shape generative process (the 6 degree-of-freedom 3D pose of the 3D shape or the parameters of the learned space). The foreground region is delineated by the zero level set of a signed distance function, and we define an energy over this region and its immediate background surroundings based on pixel-wise posterior membership probabilities. We obtain the differentials of this energy with respect to the parameters governing shape and conduct searches for the correct shape using standard non-linear minimisation techniques. This methodology first leads to a novel rigid 3D object tracker. For a known 3D shape, our optimisation here aims to find the 3D pose that leads to the 2D projection that best segments a given image. We extend our approach to track multiple objects from multiple views and propose novel enhancements at the pixel level based on temporal consistency. Finally, owing to the per pixel nature of much of the algorithm, we support our theoretical approach with a real-time GPU based implementation. We next use our rigid 3D tracker in two applications: (i) a driver assistance system, where the tracker is augmented with 2D traffic sign detections, which, unlike previous work, allows for the relevance of the traffic signs to the driver to be gauged and (ii) a robust, real time 3D hand tracker that uses data from an off-the-shelf accelerometer and articulated pose classification results from a multiclass SVM classifier. Finally, we explore deformable 2D/3D object tracking. Unlike previous works, we use a non-linear and probabilistic dimensionality reduction, called Gaussian Process Latent Variable Models, to learn spaces of shape. Segmentation becomes a minimisation of an image-driven energy function in the learned space. We can represent both 2D and 3D shapes which we compress with Fourier-based transforms, to keep inference tractable. We extend this method by learning joint shape-parameter spaces, which, novel to the literature, enable simultaneous segmentation and generic parameter recovery. These can describe anything from 3D articulated pose to eye gaze. We also propose two novel extensions to standard GP-LVM: a method to explore the multimodality in the joint space efficiently, by learning a mapping from the latent space to a space that encodes the similarity between shapes and a method for obtaining faster convergence and greater accuracy by use of a hierarchy of latent embeddings.