Visual Tracking and Dynamic Learning on the Grassmann Manifold with Inference from a Bayesian Framework and State Space Models

We propose a novel visual tracking scheme that exploits both the geometrical structure of Grassmann manifold and piecewise geodesics under a Bayesian framework. Two particle filters are alternatingly employed on the manifold. One is used for online updating the appearance subspace on the manifold using sliding-window observations, and the other is for tracking moving objects on the manifold based on the dynamic shape and appearance models. Main contributions of the paper include: (a) proposing an online manifold learning strategy by a particle filter, where a mixture of dynamic models is used for both the changes of manifold bases in the tangent plane and the piecewise geodesics on the manifold. (b) proposing a manifold object tracker by incorporating object shape in the tangent plane and the manifold prediction error of object appearance jointly in a particle filter framework. Experiments performed on videos containing significant object pose changes show very robust tracking results. The proposed scheme also shows better performance as comparing with three existing trackers in terms of tracking drift and the tightness and accuracy of tracked boxes

Detection-assisted Object Tracking by Mobile Cameras

Tracking-by-detection is a class of new tracking approaches that utilizes recent development of object detection algorithms. This type of approach performs object detection for each frame and uses data association algorithms to associate new observations to existing targets. Inspired by the core idea of the tracking-by-detection framework, we propose a new framework called detection-assisted tracking where object detection algorithm provides help to the tracking algorithm when such help is necessary; thus object detection, a very time consuming task, is performed only when needed. The proposed framework is also able to handle complicated scenarios where cameras are allowed to move, and occlusion or multiple similar objects exist. We also port the core component of the proposed framework, the detector, onto embedded smart cameras. Contrary to traditional scenarios where the smart cameras are assumed to be static, we allow the smart cameras to move around in the scene. Our approach employs histogram of oriented gradients (HOG) object detector for foreground detection, to enable more robust detection on mobile platform. Traditional background subtraction methods are not suitable for mobile platforms where the background changes constantly. Adviser: Senem Velipasalar and Mustafa Cenk Gurso

Tracking object poses in the context of robust body pose estimates

This work focuses on tracking objects being used by humans. These objects are often small, fast moving and heavily occluded by the user. Attempting to recover their 3D position and orientation over time is a challenging research problem. To make progress we appeal to the fact that these objects are often used in a consistent way. The body poses of different people using the same object tend to have similarities, and, when considered relative to those body poses, so do the respective object poses. Our intuition is that, in the context of recent advances in body-pose tracking from RGB-D data, robust object-pose tracking during human-object interactions should also be possible. We propose a combined generative and discriminative tracking framework able to follow gradual changes in object-pose over time but also able to re-initialise object-pose upon recognising distinctive body-poses. The framework is able to predict object-pose relative to a set of independent coordinate systems, each one centred upon a different part of the body. We conduct a quantitative investigation into which body parts serve as the best predictors of object-pose over the course of different interactions. We find that while object-translation should be predicted from nearby body parts, object-rotation can be more robustly predicted by using a much wider range of body parts. Our main contribution is to provide the first object-tracking system able to estimate 3D translation and orientation from RGB-D observations of human-object interactions. By tracking precise changes in object-pose, our method opens up the possibility of more detailed computational reasoning about human-object interactions and their outcomes. For example, in assistive living systems that go beyond just recognising the actions and objects involved in everyday tasks such as sweeping or drinking, to reasoning that a person has missed sweeping under the chair or not drunk enough water today. © 2014 Elsevier B.V. All rights reserved

Adaptive object segmentation and tracking

Efficient tracking of deformable objects moving with variable velocities is an important current research problem. In this thesis a robust tracking model is proposed for the automatic detection, recognition and tracking of target objects which are subject to variable orientations and velocities and are viewed under variable ambient lighting conditions. The tracking model can be applied to efficiently track fast moving vehicles and other objects in various complex scenarios. The tracking model is evaluated on both colour visible band and infra-red band video sequences acquired from the air by the Sussex police helicopter and other collaborators. The observations made validate the improved performance of the model over existing methods. The thesis is divided in three major sections. The first section details the development of an enhanced active contour for object segmentation. The second section describes an implementation of a global active contour orientation model. The third section describes the tracking model and assesses it performance on the aerial video sequences. In the first part of the thesis an enhanced active contour snake model using the difference of Gaussian (DoG) filter is reported and discussed in detail. An acquisition method based on the enhanced active contour method developed that can assist the proposed tracking system is tested. The active contour model is further enhanced by the use of a disambiguation framework designed to assist multiple object segmentation which is used to demonstrate that the enhanced active contour model can be used for robust multiple object segmentation and tracking. The active contour model developed not only facilitates the efficient update of the tracking filter but also decreases the latency involved in tracking targets in real-time. As far as computational effort is concerned, the active contour model presented improves the computational cost by 85% compared to existing active contour models. The second part of the thesis introduces the global active contour orientation (GACO) technique for statistical measurement of contoured object orientation. It is an overall object orientation measurement method which uses the proposed active contour model along with statistical measurement techniques. The use of the GACO technique, incorporating the active contour model, to measure object orientation angle is discussed in detail. A real-time door surveillance application based on the GACO technique is developed and evaluated on the i-LIDS door surveillance dataset provided by the UK Home Office. The performance results demonstrate the use of GACO to evaluate the door surveillance dataset gives a success rate of 92%. Finally, a combined approach involving the proposed active contour model and an optimal trade-off maximum average correlation height (OT-MACH) filter for tracking is presented. The implementation of methods for controlling the area of support of the OT-MACH filter is discussed in detail. The proposed active contour method as the area of support for the OT-MACH filter is shown to significantly improve the performance of the OT-MACH filter's ability to track vehicles moving within highly cluttered visible and infra-red band video sequence