14377828
Switching observation models for contour tracking in clutter
We propose a generative model approach to contour tracking against nonstationary clutter and to coping with occlusions by explicit modelling and inferring. The proposed dynamic Bayesian networks consist of multiple hidden processes, which model the target, the clutter and the occlusions. The image observation models, which depict the generation of the image features, are conditioned on all the hidden processes. Based on this framework, the tracker can automatically switch among different observation models according to the hidden states of the clutter and occlusions. In addition, the inference of these hidden states provides self-evaluations for the tracker. The tracking and inference are implemented based on sequence Monte Carlo techniques. The effectiveness of the proposed approach to robust tracking and inferring nonstationary clutter and occlusion is demonstrated for a variety of image sequences.
2239318
Decentralized multiple target tracking using netted collaborative autonomous trackers
This paper presents a decentralized approach to multiple target tracking. The novelty of this approach lies in the use of a set of autonomous while collaborative trackers to overcome the tracker coalescence problem with linear complexity. In this approach, the individual trackers are autonomous in the sense that they can select targets to track and evaluate themselves, and they are also collaborative since they need to compete for the targets against those trackers that are close to them through communication. The theoretical foundation of this new approach is based on the variational analysis of a Markov network that reveals the collaborative mechanism through fixed point iteration among these trackers and the existence of the equilibriums. In addition, a trained object detector is incorporated to help sense the potential newly appearing targets in the dynamic scene. Experimental results on challenging video sequences demonstrate the effectiveness and efficiency of the proposed method.
61674575
Collaborative tracking of multiple targets
Coalescence, meaning the tracker associates more than one trajectories to some targets while loses track for others, is a challenging problem for visual tracking of multiple targets, especially when similar targets move close or present occlusions. Existing approaches that are based on joint data association are confronted by the combinatorial complexity due to the concatenation of the state spaces of individual targets. This paper presents a novel collaborative approach with linear complexity to the coalescence problem. The basic idea is the collaborative inference mechanism, in which the estimate of an individual target is not only determined by its own observation and dynamics, but also through the interaction and collaboration with the estimates of its adjacent targets, which leads to a competition mechanism that enables different targets to compete for the common image observations. The theoretical foundation of the new approach is based on Markov networks. Variational analysis of this Markov network reveals a mean field approximation to the posterior density of each target, therefore provides a computationally efficient way for such a difficult inference problem. In addition, a mean field Monte Carlo (MFMC) algorithm is designed to achieve Bayesian inference by simulating the competition among a set of low dimensional particle filters. Compared with the existing solutions, the proposed new collaborative approach stands out by its effectiveness and low computational cost to the coalescence problem, as pronounced in the extensive experiments.
7820656
Tracking appearances with occlusions
Occlusion is a difficult problem for appearance-based target tracking, especially when we need to track multiple targets simultaneously and maintain the target identities during tracking. To cope with the occlusion problem explicitly, this paper proposes a dynamic Bayesian network, which accommodates an extra hidden process for occlusion and stipulates the conditions on which the image observation likelihood is calculated. The statistical inference of such a hidden process can reveal the occlusion relations among different targets, which makes the tracker more robust against partial even complete occlusions. In addition, considering the fact that target appearances change with views, another generative model for multiple view representation is proposed by adding a switching variable to select from different view templates. The integration of the occlusion model and multiple view model results in a complex dynamic Bayesian network, where extra hidden processes describe the switch of targets' templates, the targets' dynamics, and the occlusions among different targets. The tracking and inferring algorithms are implemented by the sampling-based sequential Monte Carlo strategies. Our experiments show the effectiveness of the proposed probabilistic models and the algorithms.
206764155
A field model for human detection and tracking
The large shape variability and partial occlusions challenge most object detection and tracking methods for nonrigid targets such as pedestrians. This paper presents a new approach based on a two-layer statistical field model that characterizes the prior of the complex shape variations as a Boltzmann distribution and embeds this prior and the complex image likelihood into a Markov field. A probabilistic variational analysis of this model reveals a set of fixed-point equations characterizing the equilibrium of the field. It leads to computationally efficient methods for calculating the image likelihood and for training the model. Based on that, effective algorithms for detecting nonrigid objects are developed. This new approach has several advantages. First, it is intrinsically suitable for capturing local nonrigidity. In addition, due to the distributed likelihood, this approach is robust to partial occlusions. Moreover, the two-layer structure provides large flexibility of modeling the image observations, which makes the new method robust to clutters. Extensive experiments demonstrate its effectiveness.
5363138
On the modeling and analysis of obligations
Traditional security policies largely focus on access control requirements, which specify who can access what under what circumstances. Besides access control requirements, the availability of services in many applications often further imposes obligation requirements, which specify what actions have to be taken by a subject in the future as a condition of getting certain privileges at present. However, it is not clear yet what the implications of obligation policies are concerning the security goals of a system.In this paper, we propose a formal metamodel that captures the key aspects of a system that are relevant to obligation management. We formally investigate the interpretation of security policies from the perspective of obligations, and define secure system states based on the concept of accountability. We also study the complexity of checking a state's accountability under different assumptions about a system.
415460
Model Driven Development of Business Process Monitoring and Control Systems
This paper describes a model-driven approach in monitoring and controlling the behaviour of business processes. The business-level monitoring and control requirements are first described by a series of policies that can be combined together to construct a Directed Acyclic Graph (DAG), which can be regarded as the Platform Independent Model (PIM) for the high level business solution. PIM provides a convenient and clear way for business users to understand, monitor and control the interactions in the target business process. Then the PIM is transformed to an executable representation (Platform Specific Model, PSM), such as BPEL (Business Process Execution Language for Web Service) by decomposing the DAG into several sub-processes and modelling each sub-process as a BPEL process that will be deployed to runtime.
1690622
Aggregate Query Answering on Anonymized Tables
Privacy is a serious concern when microdata need to be released for ad hoc analyses. The privacy goals of existing privacy protection approaches (e.g., k-anonymity and l-diversity) are suitable only for categorical sensitive attributes. Since applying them directly to numerical sensitive attributes (e.g., salary) may result in undesirable information leakage, we propose privacy goals to better capture the need of privacy protection for numerical sensitive attributes. Complementing the desire for privacy is the need to support ad hoc aggregate analyses over microdata. Existing generalization-based anonymization approaches cannot answer aggregate queries with reasonable accuracy. We present a general framework of permutation-based anonymization to support accurate answering of aggregate queries and show that, for the same grouping, permutation-based techniques can always answer aggregate queries more accurately than generalization-based approaches. We further propose several criteria to optimize permutations for accurate answering of aggregate queries, and develop efficient algorithms for each criterion.
2350428
Interoperable strategies in automated trust negotiation
Automated trust negotiation is an approach to establishing trust between strangers through the exchange of digital credentials and the use of access control policies that specify what combinations of credentials a stranger must disclose in order to gain access to each local service or credential. We introduce the concept of a trust negotiation protocol, which defines the ordering of messages and the type of information messages will contain. To carry out trust negotiation, a party pairs its negotiation protocol with a trust negotiation strategy that controls the exact content of the messages, i.e., which credentials to disclose, when to disclose them, and when to terminate a negotiation. There are a huge number of possible strategies for negotiating trust, each with different properties with respect to speed of negotiations and caution in giving out credentials and policies. In the autonomous world of the Internet, entities will want the freedom to choose negotiation strategies that meet their own goals, which means that two strangers who negotiate trust will often not use the same strategy. To date, only a tiny fraction of the space of possible negotiation strategies has been explored, and no two of the strategies proposed so far will interoperate. In this paper, we define a large set of strategies called the disclosure tree strategy (DTS) family. Then we prove that if two parties each choose strategies from the DTS family, then they will be able to negotiate trust as well as if they were both using the same strategy. Further, they can change strategies at any point during negotiation. We also show that the DTS family is closed, i.e., any strategy that can interoperate with every strategy in the DTS family must also be a member of the DTS family. We also give examples of practical strategies that belong to the DTS family and fit within the TrustBuilder architecture and protocol for trust negotiation.
5576187
An Analysis of Several Software Defect Models
Results are presented of an analysis of several defect models using data collected from two large commercial projects. Traditional models typically use either program matrices (i.e. measurements from software products) or testing time or combinations of these as independent variables. The limitations of such models have been well-documented. The models considered use the number of defects detected in the earlier phases of the development process as the independent variable. This number can be used to predict the number of defects to be detected later, even in modified software products. A strong correlation between the number of earlier defects and that of later ones was found. Using this relationship, a mathematical model was derived which may be used to estimate the number of defects remaining in software. This defect model may also be used to guide software developers in evaluating the effectiveness of the software development and testing processes. >