A statistical field model for pedestrian detection
This paper presents a new statistical model for detecting and tracking deformable objects such as pedestrians, where large shape variations induced by local shape deformation can not be well captured by global methods such as PCA. The proposed model employs a Boltzmann distribution to capture the prior of local deformation, and embeds it into a Markov network which can be learned from data. A mean field variational analysis of this model provides computationally efficient algorithms for computing the likelihood of image observations and facilitate fast model training. Based on that, effective detection and tracking algorithms for deformable objects are proposed and applied to pedestrian detection and tracking. The proposed method has several advantages. Firstly, it captures local deformation well and thus is robust to occlusions and clutter. In addition, it is computationally tractable. Moreover, it divides deformation into local deformation and global deformation, then conquers them by combining bottom-up and top-down methodologies. Extensive experiments demonstrate the effectiveness of the proposed model for deformable objects.

title: Decentralized multiple target tracking using netted collaborative autonomous trackers
abstract: 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.

title: A field model for human detection and tracking
abstract: 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.

title: Switching observation models for contour tracking in clutter
abstract: 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.

title: Tracking appearances with occlusions
abstract: 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.

title: Collaborative tracking of multiple targets
abstract: 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.

title: Negotiating Trust on the Web
abstract: Our system, TrustBuilder, supports automated trust negotiation between strangers on the Internet. TrustBuilder lets negotiating parties disclose relevant digital credentials and access control policies and establish the trust necessary to complete their interaction. TrustBuilder is intended for use in any situation where two entities from different security domains need to establish trust - business-to-business and retail interactions, cooperative work and joint ventures, medical records, mobile computing, and so on.

title: Jet subdivision schemes on the k-regular complex
abstract: We introduce a new family of subdivision schemes called jet subdivision schemes. Jet subdivision schemes are a natural generalization of the commonly used subdivision schemes for free-form surface modeling. In an order r jet subdivision scheme, rth order Taylor expansions, or r-jets, of functions are the essential objects being generated in a coarse-to-fine fashion. Standard subdivision surface methods correspond to the case r=0. Just as the standard free-form subdivision surface schemes, jet subdivision schemes are based on combining (i) a symmetric subdivision scheme in the shift-invariant setting with (ii) an extraordinary vertex rule. We formulate the notions of stationarity, symmetry, and smoothness for jet subdivision schemes. We then extend some well-known results in the theory of subdivision surfaces to the setting of jet subdivision schemes.By incorporating high order data into the subdivision rules, jet subdivision schemes offer more degrees of freedom for the design of extraordinary vertex rules than do standard subdivision schemes. Using a simple 2-point stencil, we construct an order 1 jet subdivision scheme, which is interpolatory, C1 everywhere, and free from polar artifacts at extraordinary vertices of any valence. It is similar to the popular butterfly scheme, but unlike the butterfly scheme, it produces surfaces that can be explicitly parameterized by spline functions. In addition, our jet subdivision scheme allows for explicit control of gradient information on the resulting surface.We address the problem of constructing curvature continuous subdivision schemes at extraordinary vertices by giving an example of a 'flexible C2 scheme' for extraordinary vertex of valence k = 3. The stencils for this subdivision scheme coincide with those of the popular Loop scheme. We discuss the mathematical structure of this example of a C2 scheme.

title: Index-based approximate XML joins
abstract: XML data integration tools are facing a variety of challenges for their efficient and effective operation. Among these is the requirement to handle a variety of inconsistencies or mistakes present in the data sets. We study the problem of integrating XML data sources through index assisted join operations, using notions of approximate match in the structure and content of XML documents as the join predicate. We show how a well known and widely deployed index structure, namely the R-tree, can be adopted to improve the performance of such operations. We propose novel search and join algorithms for R-trees adopted to index XML document collections. We also propose novel optimization objectives for R-tree construction, making R-trees better suited for this application.

title: Service Selection Algorithms for Composing Complex Services with Multiple QoS Constraints
abstract: One of the promises of the service-oriented architecture (SOA) is that complex services can be composed using individual services. Individual services can be selected and integrated either statically or dynamically based on the service functionalities and performance constraints. For many distributed applications, the runtime performance (e.g. end-to-end delay, cost, reliability and availability) of complex services are very important. In our earlier work, we have studied the service selection problem for complex services with only one QoS constraint. This paper extends the service selection problem to multiple QoS constraints. The problem can be modelled in two ways: the combinatorial model and the graph model. The combinatorial model defines the problem as the multi-dimension multi-choice 0-1 knapsack problem (MMKP). The graph model defines the problem as the multi-constraint optimal path (MCOP) problem. We propose algorithms for both models and study their performances by test cases. We also compare the pros & cons between the two models.

title: Approximate XML joins
abstract: XML is widely recognized as the data interchange standard for tomorrow, because of its ability to represent data from a wide variety sources. Hence, XML is likely to be the format through which data from multiple sources is integrated.In this paper we study the problem of integrating XML data sources through correlations realized as join operations. A challenging aspect of this operation is the XML document structure. Two documents might convey approximately or exactly the same information but may be quite different in structure. Consequently approximate match in structure, in addition to, content has to be folded in the join operation. We quantify approximate match in structure and content using well defined notions of distance. For structure, we propose computationally inexpensive lower and upper bounds for the tree edit distance metric between two trees. We then show how the tree edit distance, and other metrics that quantify distance between trees, can be incorporated in a join framework. We introduce the notion of reference sets to facilitate this operation. Intuitively, a reference set consists of data elements used to project the data space. We characterize what constitutes a good choice of a reference set and we propose sampling based algorithms to identify them. This gives rise to a variety of algorithmic approaches for the problem, which we formulate and analyze. We demonstrate the practical utility of our solutions using large collections of real and synthetic XML data sets.