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.
35689052
Visual contour tracking based on sequential importance sampling/resampling algorithm
The condensation algorithm can deal with non-Gaussian, nonlinear visual contour tracking in a unified way. Despite its simple implementation and generality, it has two main limitations. The first limitation is that in sampling stage the algorithm does not take advantage of the new measurements. As a result of the inefficient sampling strategy, the algorithm needs a large number of samples to represent the posterior distribution of state. The next is in the selection step, resampling may introduce the problem of sample impoverishment. To address these two problems, we present an improved visual tracker based on an importance sampling/resampling algorithm. Gaussian density of each sample is adopted as the sub-optimal importance proposal distribution, which can steer the samples towards the high likelihood by considering the latest observations. We also adopt a criterion of effective sample size to determine whether the resampling is necessary or not. Experiments with real image sequences show that the performance of new algorithm improves considerably for tracking in visual clutter.
9618917
Towards A Robust and Real-time Face Detection and Tracking Framework
Traditional research and development in face detection and tracking are mostly focused on either face detection in still images, or face tracking in image streams, without remarkable combination of both. As a result, few impressive face detection and tracking systems can be seen nowadays. The paper contributes to effectively incorporation of two modules, detection module and tracking module, into one integrated and coupled framework. In the detection module we construct AdaBoost detectors for face detection in video stream, which are able to detect multi-view faces and are robust to illumination changes in video stream. The tracking module is based on the improved mean shift tracking, which has advantage of target model adaption. The detection module, triggered every ten frames, is not only responsible for initiating a tracking thread, but also, together with tracking module, contributes to target model update. The system is able to benefit from the strengths of different techniques and overcome their respective limitations. Experiments on real image sequences show that the system is robust and real-time in realistic environment.
6242008
Variations of the maximum leaf spanning tree problem for bipartite graphs
The maximum leaf spanning tree problem is known to be NP-complete. In [M.S. Rahman, M. Kaykobad, Complexities of some interesting problems on spanning trees, Inform. Process. Lett. 94 (2005) 93-97], a variation on this problem was posed. This variation restricts the problem to bipartite graphs and asks, for a fixed integer K, whether or not the graph contains a spanning tree with at least K leaves in one of the partite sets. We show not only that this problem is NP-complete, but that it remains NP-complete for planar bipartite graphs of maximum degree 4. We also consider a generalization of a related decision problem, which is known to be polynomial-time solvable. We show the problem is still polynomial-time solvable when generalized to weighted graphs.
14712997
Proactive QoS negotiation in asynchronous real-time distributed systems
We present a fast, proactive, quality of service (QoS) negotiation algorithm called Best Effort Negotiation (or BEN), for asynchronous real-time distributed systems. BEN considers an application model where trans-node application timeliness and fault-tolerance requirements are expressed using benefit functions, and anticipated workload and system failure rates during future time intervals are expressed using adaptation functions and reliability functions, respectively. Furthermore, BEN considers an adaptation model where subtasks of application tasks are replicated at run-time for tolerating failures as well as for sharing workload increases. Given such models, the objective of the algorithm is to maximize the sum of aggregate real-time and fault-tolerance benefits during the time window of adaptation functions. Since determining the optimal solution is computationally intractable, BEN heuristically computer sub-optimal resource allocations in polynomial-time. To determine how well BEN performs, we describe another algorithm called HLC, that is inspired by the well-known Hill Climbing heuristic. We show that HLC is significantly slower than BEN. However, our experimental studies reveal that the performance of BEN, in general, is as good as that of HLC.
13285231
Analog and RF circuit macromodels for system-level analysis
Design and validation of mixed-signal integrated systems require system-level model abstractions. Generalized Volterra series based models have been successfully applied for analog and RF component macromodels, but their complexity can sometimes limit their utility for time-varying systems and large circuits with complex device models or numerous parasitics. In this paper we propose simple and efficient analog and RF circuit macromodels that provide accurate model abstractions for large, complex time-varying circuits over frequency bands of interest. By starting with the system-level block diagram model structures and focusing on the narrow RF bands, the proposed macromodels can efficiently capture the nonlinear behavior as well as the impact of RLC coupling parasitics via compact reduced-order model forms. While the macromodel can trade accuracy for simplicity in terms of the number of frequency expansion points, we find that expansion about one frequency point provides the accuracy required for system-level analysis of most RF and narrow-band analog components. The macromodel form corresponds to block diagram structures that are easily incorporated into our system-level simulation tool based on Simulink.
605614
Noise macromodel for radio frequency integrated circuits
Noise performance is a critical analog and RF circuit design constraint, and can impact the selection of the IC system-level architecture. It is therefore imperative that some model of the noise is represented at the highest levels of abstraction during the design process. In this paper we propose a noise macromodel for analog circuits and demonstrate it by way of implementation in a system level simulator based on MATLAB. We also explain our process of macromodel extraction via reformulation of frequency-domain noise analysis results, and the corresponding steps of model order reduction. The results demonstrate the efficacy of this macromodel for frequency domain system level simulation.
6919037
A systems engineering approach for constructing certifiable real-time distributed systems
In this paper, we present a systems engineering methodology for constructing certifiable realtime distributed systems. In the proposed approach, an architectural and algorithmic solution to an application problem is designed by considering the "weakest" models including the weakest asynchronous computational model and multimodal arrival model. Furthermore, timeliness properties are described using Jensen's benefit accrual predicates. Once a system solution is designed, timeliness properties are established by constructing necessary feasibility conditions that are expressed as non-valued predicates. The predicates are quantified and verified to produce the specification of a certified solution. We illustrate the approach by considering a packet transmission problem that desire soft timeliness. We present a certifiable solution to this problem that consists of switched Ethernet, a soft real-time packet scheduling algorithm (that was previously developed), and feasibility conditions.