13006853
Novel likelihood estimation technique based on boosting detector
This paper presents novel likelihood estimation to be used for particle filter based object tracking. The likelihood estimation is built upon cascade object detector trained with Gentle AdaBoost (GAB), in order to capture the probability of existence of object. Two strategies are adopted to construct the likelihood functions: probability-intra-stage (PIS) corresponding to real output of each weak classifier in the same stage, and probability-outer-stage (POS) corresponding to the depth reached in the cascade detector. Five kinds of likelihood functions are thus proposed based on the trained GAB detector. Our experiment shows the likelihood functions are able to characterize probabilistically the existence of object accurately, having much higher confidence value in object regions than that in background, and that the integral strategy of PIS and POS is the best choice.
12395366
Image Cues Fusion for Object Tracking Based on Particle Filter
Particle filter is a powerful algorithm to deal with non-linear and non-Gaussian tracking problems. However the algorithm relying only upon one image cue often fails in challenging scenarios. To overcome this, the paper first presents a color likelihood to capture color distribution of the object based on Bhattacharry coefficient, and a structure likelihood representing high level knowledge regarding the object. Together with the widely used edge likelihood, the paper further proposes a straightforward image cues fusion for object tracking in the framework of particle filter, under assumption that the visual measurement of each image cue is independent of each other. The experiments on real image sequences have shown that the method is effective, robust to illumination changes, pose variations and complex background.
18829609
Proposal of Novel Histogram Features for Face Detection
This paper presents novel features for face detection in the paradigm of AdaBoost algorithm. Features are multi-dimensional histograms computed from a set of rectangles in the filtered images, and they represent marginal distributions of these rectangles. The filter banks consist of intensity, Laplacian of Gaussian (Difference of Gaussians), and Gabor filters, aiming at capturing spatial and frequency properties of human faces at different scales and different orientations. The best features selected by AdaBoost, pairs of filter and rectangle, can thus be interpreted as boosted marginal distributions of human faces. The result of preliminary experiments demonstrate that the selected features are much more powerful to describe the face pattern than the simple features of Viola and Jones and some variants which can only capture several moments of ONE dimensional histogram in intensity images.
42009299
Probabilistic Object Tracking Based on Machine Learning and Importance Sampling
The paper presents a novel particle filtering framework for visual object tracking. One of the contributions is the development of a likelihood function based on one of machine learning algorithm–AdaBoost algorithm. The likelihood function can capture the structure characteristics of one class of objects, and is thus robust to clutters and noise in the complex background. The other contribution is the adoption of mean shift iteration as a proposal distribution, which can steer discrete samples towards regions which most likely contain the targets, and is therefore leading to computational efficiency in the algorithm. The effectiveness of such a framework is demonstrated with a particular class of objects–human faces.
43248584
Histogram Features-Based Fisher Linear Discriminant for Face Detection
The face pattern is described by pairs of template-based histogram and Fisher projection orientation under the paradigm of AdaBoost learning in this paper. We assume that a set of templates are available first. To avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize the response of each template. By introducing a novel “integral histogram image”, we can compute histograms rapidly. Then we turn to Fisher linear discriminant for each template to project histograms from d–dimensional to one-dimensional subspace. Best features, used to describe face pattern, are selected by AdaBoost learning. The results of preliminary experiments demonstrate that the selected features are much more powerful to represent the face pattern than the simple rectangle features used by Viola and Jones and some variants.
1068580
Visual contour tracking based on particle filters
Abstract The Condensation algorithm, developed for visual tracking, is a variant of particle filter. In the sampling stage of Condensation, no use is made of the information from the current frame in the image sequence. As a consequence, the algorithm requires a large number of particles and is computationally expensive. In this paper, a Kalman particle filter (KPF) and an unscented particle filter (UPF) are applied to contour tracking to try to overcome the problem. These algorithms use the Kalman filter or unscented Kalman filter to incorporate information from the current frame. This sampling strategy can effectively steer the set of particles towards regions with high likelihood, and therefore can considerably reduce the number of particles needed for tracking. Performance comparisons show that the KPF is an improvement over Condensation, while the UPF has a much higher computational cost for equal tracking error.
259942
Scheduling distributable real-time threads in Tempus middleware
This paper presents the Tempus real-time middleware, which supports real-time CORBA 2.0's distributable threads (DTs) as an end-to-end programming abstraction for distributed real-time systems. DTs in Tempus can have time constraints, including time/utility functions (TUFs), can have resource constraints, including mutual exclusion, and can be scheduled according to utility accrual (UA) disciplines. Tempus propagates the scheduling parameters of DT's as they transit objects and hence perhaps node boundaries. Node-local instances of a UA scheduling algorithm use the propagated parameters to construct local schedules and resolve resource dependencies for local timeliness optimization, toward approximate, system-wide timeliness optimality. Tempus uses an application-level scheduling framework for node-local TUF/UA scheduling on real-time POSIX-compliant operating systems. Our experimental measurements demonstrate the effectiveness of the middleware in scheduling DTs.
46149042
Data reconciliation for real-time optimization of an industrial coke-oven-gas purification process
Online optimization is more and more used in the chemical industry to run a process near its optimum operating condition by providing real-time computed optimal set-points to the distributed control system. Process measurements are necessary for these applications to determine the actual state of the process and to increase the accuracy of the model with parameter estimation techniques. However, these measurements usually contain random as well as gross errors which have to be identified and eliminated before the measurements are used for online optimization. In this contribution, a data reconciliation approach was integrated into an online optimization framework for the ammonia hydrogen sulfide circulation scrubbing, a common industrial coke-oven-gas purification process. We used a rigorous rate-based model to describe this reactive absorption and desorption process. To increase the accuracy of the model, we estimated several process parameters using a sequential parameter estimation approach. Data reconciliation was performed based on simple component balances to achieve model-consistent data and to identify measurement biases. The model was then validated online on a pilot plant by connecting the estimation package through the process control system. Based on the online measured data, operating cost minimization was carried out and the computed optimal set-points realized real-time. A satisfactory agreement between measured data and optimization was achieved.
14201193
Adaptive time-critical resource management using time/utility functions: past, present, and future
Time/utility function time constraints (or TUFs) and utility accrual (UA) scheduling optimality criteria, constitute, arguably, the most effective and broadest approach for adaptive, dynamic time-critical resource management. A TUF, which is a generalization of the classical deadline constraint, specifies the utility of completing an application activity as an application-or situation-specific function of that activity's completion time. With TUF time constraints, timeliness optimality criteria can be specified in terms of accrued (e.g., summed) activity utilities. This work overviews past and recent advances on adaptive resource management for dynamic time-critical systems using UA algorithms. Emerging challenges and new research directions are also identified.
10769697
Advanced control flow in Java card programming
Java Card technology simplifies the development of smart card applications by providing a high-level programming language similar to Java. However, the master-slave programming model used in current Java Card platform creates control flow difficulties when writing complex card programs, making it inconvenient, tedious, and error-prone to implement Java Card applications. This paper examines these drawbacks of the master-slave model and proposes a concurrent thread model for developing future Java Card programs, which is much closer to conventional Java network programming. This paper also presents a code translation algorithm and a corresponding tool that makes it possible to write card programs in the concurrent thread model without losing compatibility with the existing Java Card API.
42771841
Passive velocity field control of mechanical manipulators
Two concepts for the task specification and control for mechanical manipulators are advocated: the coding of tasks in terms of velocity fields; and controllers that maintain the passivity relationship between the manipulator and its physical environment. A passive velocity field tracking controller is proposed. The proposed dynamic controller mimics a flywheel: it stores and releases energy to the manipulator, but does not generate any. The controller has the interesting property that it stabilizes any multiple (positive or negative) of the desired velocity field, and exponentially stabilizes the particular multiple which is related to the total kinetic energy of the manipulator+controller.