13502244
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
Decomposing video frames into coherent 2D motion layers is a powerful method for representing videos. Such a representation provides an intermediate description that enables applications such as object tracking, video summarization and visualization, video insertion, and sprite-based video compression. Previous work on motion layer analysis has largely concentrated on two-frame or multi-frame batch formulations. The temporal coherency of motion layers and the domain constraints on shapes have not been exploited. This paper introduces a complete dynamic motion layer representation in which spatial and temporal constraints on shape, motion and layer appearance are modeled and estimated in a maximum a-posteriori (MAP) framework using the generalized expectation-maximization (EM) algorithm. In order to limit the computational complexity of tracking arbitrarily shaped layer ownership, we propose a shape prior that parameterizes the representation of shape and prevents motion layers from evolving into arbitrary shapes. In this work, a Gaussian shape prior is chosen to specifically develop a near-real-time tracker for vehicle tracking in aerial videos. However, the general idea of using a parametric shape representation as part of the state of a tracker is a powerful one that can be extended to other domains as well. Based on the dynamic layer representation, an iterative algorithm is developed for continuous object tracking over time. The proposed method has been successfully applied in an airborne vehicle tracking system. Its performance is compared with that of a correlation-based tracker and a motion change-based tracker to demonstrate the advantages of the new method. Examples of tracking when the backgrounds are cluttered and the vehicles undergo various rigid motions and complex interactions such as passing, turning, and stop-and-go demonstrate the strength of the complete dynamic layer representation.
39247721
True Multi-Image Alignment and Its Application to Mosaicing and Lens Distortion Correction
Multiple images of a scene are related through 2D/3D view transformations and linear and nonlinear camera transformations. We present an algorithm for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. The algorithm is developed to specifically align and mosaic images using parametric transformations in the presence of lens distortion. When lens distortion is present, none of the images can be assumed to be ideal. In our formulation, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation. The goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images. Key advantages of the technique presented in this paper are: (i) no reliance on one distortion free image, (ii) ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence, and (iii) ability to handle linear and nonlinear transformations within the same framework. Results of applying the algorithm are presented for the correction of lens distortion, and creation of video mosaics.
31836660
A Global Matching Framework for Stereo Computation
This paper presents a new global matching framework f o r stereo computation. In this framework, the second view is first predicted front the reference view using the depth information. A global match measure is then defined as the similarity function between the predicted image and the actual image. Stereo coniputcction is converted into a search problem where the goal is to find the depth rnup that maximizes the global match measure. The major advantage of this framework is that the global visibility constraint is inherently enforced in the computation. This paper explores several key components of this framework including ( I ) three color segmentation based depth representations, (2) an incremental warping algorithm that dramatically reduces the computational complexity, and (3) scene constraints such as the smoothness constraint and the color sinzilarity constraint. Experimental results using different types of depth representations are presented. The quality of the computed depth maps is demonstrated through image-based rendering from new viewpoints.
6283962
Multi-view 3D analysis with applications for augmented reality and enhanced video visualization
The article presents methods for 3D scene geometry recovery/refinement and pose estimation from motion imagery in two representative scenarios. First, we present a method for pose estimation and scene geometry recovery from extended sequences without prior knowledge of the scheme. Second, we discuss how to recover camera poses when a rough scene model is provided. We show how to extend and refine the scene model using the recovered poses. Finally, we present applications of the above techniques for 3D imagery manipulation such as enhanced visualization for video and 3D insertion of synthetic objects in the imagery.
14585589
Real-time wide area multi-camera stereo tracking
We present a fully integrated real-time system to track humans with a network of stereo sensors over a wide area. The processing includes single camera tracking and multi-camera fusion. Each single camera detects and tracks humans in its own view and a multi-camera fusion module combines all the local tracks of the same human into a global track. We propose stereo segmentation and tracking techniques to handle multiple humans moving in groups in cluttered environments. We have developed a ground-based fusion method for camera handoff using space-time constraint. We show results and performance evaluation on very challenging data from a 12-camera system.
25332181
A Heterogeneous Feature-based Image Alignment Method
In this paper, we propose a robust heterogeneous feature based image alignment method that utilizes points, lines and regions in a unified framework. The image motion is decomposed into progressively complex components, i.e., translation, similarity, affine, and projective motion models, and alignment is obtained with deliberatively selected suitable feature types and associated descriptors. Large convergence range is obtained by gradually constraining the search range of features in each stage. Notably, point and line features are jointly used and formulated in a RANSAC (random sample consensus) framework for robust estimation of a homography between low textured images. Further improvement is obtained with region based direct method. Experiments demonstrate superior alignment results of our approach to both gradient-based direct method and tradition point feature based alignment method
16117863
A Comparative Study of Different Sensors for Smart Car Park Management
Wireless sensor networks offer a very attractive, low cost alternative for traffic monitoring and surveillance in car parking, freeways and intersection in different environmental conditions. In such systems, robust and reliable vehicle detection is a critical step, thus it is important to carefully evaluate the different modes of operations in order to address existing issues. This paper proposes a vision of improvements to wireless sensor car parking network system by presenting valuable comparison data for different sensors using different type of software functions and hardware components. The main goal of this work is to present a simple, automated analysis and comparison of power management, communication efficiency and threshold times using different sensors and a corresponding prediction of the above features. The results show a clear difference in various parameters by using different types of sensors for vehicle detection.
1384745
Exploiting unbalanced thread scheduling for energy and performance on a CMP of SMT processors
This paper explores thread scheduling on an increasingly popular architecture: chip multiprocessors with simultaneous multithreading cores. Conventional multiprocessor scheduling, applied to this architecture, will attempt to balance the thread load across cores. This research demonstrates that such an approach eliminates one of the big advantages of this architecture - the ability to use unbalanced schedules to allocate the right amount of execution resources to each thread. However, accommodating unbalanced schedules creates several difficulties, the biggest being the fact that the search space of all schedules (both balanced and unbalanced) is much greater than that of the balanced schedules alone. This work proposes and evaluates scheduling policies that allow the system to identify and migrate toward good thread schedules, whether the best schedules are balanced or unbalanced
6867265
Servo: a programming model for many-core computing
Conventional programming models were designed to be used by expert programmers for programming for large-scale multiprocessors, distributed computational clusters, or specialized parallel machines. These models, therefore, are deemed either too difficult for an "average" programmer (who will be expected to do parallel programming in the many-core world) or too inefficient to use for many-core architectures [3]. Similarly, conventional execution models were designed for performance, not scalability.
 To address the challenge of performance scalability for many-core architectures, we introduce Servo, a Service Oriented programming model that decomposes every program into a set of components (each with its own local memory, mutable state, and a program counter) that either request services or deliver services. Servo is characterized by the decoupling of logical communication mapping between program modules (or services) from physical communication mapping between modules. This allows the services to be migrated and replicated during execution. The proposed model also allows the granularity of data parallel operations to be changed dynamically. This allows runtime variation in the locking granularity which in turn enables higher write-parallelism. Finally, the models partition even data into services. This can significantly enhance locality and makes it possible to have superlinear speedups with increasing number of cores.
 Our preliminary investigations demonstrate significant performance scalability advantages for Servo.
13392220
Introduction to the special issue on the 2007 workshop on design, analysis, and simulation of chip multiprocessors (dasCMP'07)
Chip multiprocessor architectures are becoming increasingly attractive as an option to provide high instruction throughput while keeping power and complexity under control. Such architectures have also been shown to have scalability and productivity advantages. Multi-core processors are fast becoming mainstream.
65135986
Model dependent inference of three-dimensional information from a sequence of two-dimensional images
In order to autonomously navigate through a complex environment, a mobile robot requires sensory feedback. This feedback will typically include the 3D motion and location of the robot and the 3D structure and motion of obstacles and other environmental features. The general problem considered in this thesis is how this 3D information may be obtained from a sequence of images generated by a camera mounted on a mobile robot. 
The first set of algorithms developed in this thesis are for robust determination of the 3D pose of the mobile robot from a matched set of model and image landmark features. Least-squares techniques for point and line tokens, which minimize both rotation and translation simultaneously are developed and shown to be far superior to the earlier techniques which solved for rotation first and then translation. However, least-squares techniques fail catastrophically when outliers (or gross errors) are present in the match data. Outliers arise frequently due to incorrect correspondences or gross errors in the 3D model. Robust techniques for pose determination are developed to handle data contaminated by fewer than 50.0% outliers. 
To make the model based approach widely applicable, it is necessary to be able to automatically build the landmark models. The approach adopted in this thesis is one of model extension and refinement. A partial model of the environment is assumed to exist and this model is extended over a sequence of frames. As will be shown in the experiments, the prior knowledge of the small partial model greatly enhances the robustness of the 3D structure computations. The initial 3D model may have errors and these too are refined over the sequence of frames. 
Finally, the sensitivity of pose determination and model extension to incorrect estimates of camera parameters is analyzed. It is shown that for small field of view systems, offsets in the image center do not significantly affect the location of the camera and the location of new 3D points in a world coordinate system. Errors in the focal length significantly affect only the component of translation along the optical axis in the pose computation.