Robust visual tracking based on online learning sparse representation

Handling appearance variations is a very challenging problem for visual tracking. Existing methods usually solve this problem by relying on an effective appearance model with two features: (1) being capable of discriminating the tracked target from its background, (2) being robust to the target's appearance variations during tracking. Instead of integrating the two requirements into the appearance model, in this paper, we propose a tracking method that deals with these problems separately based on sparse representation in a particle filter framework. Each target candidate defined by a particle is linearly represented by the target and background templates with an additive representation error. Discriminating the target from its background is achieved by activating the target templates or the background templates in the linear system in a competitive manner. The target's appearance variations are directly modeled as the representation error. An online algorithm is used to learn the basis functions that sparsely span the representation error. The linear system is solved via ℓ1 minimization. The candidate with the smallest reconstruction error using the target templates is selected as the tracking result. We test the proposed approach using four sequences with heavy occlusions, large pose variations, drastic illumination changes and low foreground-background contrast. The proposed approach shows excellent performance in comparison with two latest state-of-the-art trackers.

Design and analysis of matching circuit architectures for a closest match lookup

This paper investigates the implementation of a number of circuits used to perform a high speed closest value match lookup. The design is targeted particularly for use in a search trie, as used in various networking lookup applications, but can be applied to many other areas where such a match is required. A range of different designs have been considered and implemented on FPGA. A detailed description of the architectures investigated is followed by an analysis of the synthesis results. © 2006 IEEE.

Understanding Online Audio-Visual Content: A European Initiative, Media Literacy and the User

Recent debates about media literacy and the internet have begun to acknowledge the importance of active user-engagement and interaction. It is not enough simply to access material online, but also to comment upon it and re-use. Yet how do these new user expectations fit within digital initiatives which increase access to audio-visual-content but which prioritise access and preservation of archives and online research rather than active user-engagement? This article will address these issues of media literacy in relation to audio-visual content. It will consider how these issues are currently being addressed, focusing particularly on the high-profile European initiative EUscreen. EUscreen brings together 20 European television archives into a single searchable database of over 40,000 digital items. Yet creative re-use restrictions and copyright issues prevent users from re-working the material they find on the site. Instead of re-use, EUscreen instead offers access and detailed contextualisation of its collection of material. But if the emphasis for resources within an online environment rests no longer upon access but on user-engagement, what does EUscreen and similar sites offer to different users?

Regularized Online Mixture of Gaussians for Background Subtraction

Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1 norm, we derived a new set of updating equations for l1 regularized online MoG. It is shown empirically that l1 regularized online MoG converge faster than the original online MoG .

The Stormont Papers: 50 years of Northern Ireland Debates Online

This website offers access to the Parliamentary Debates of the devolved government of Northern Ireland from June 7 1921 to the dissolution of Parliament in March 28 1972.

These papers cast a unique and valuable light on the development of the Province. The 92,000 printed pages of Parliamentary Debates are held by few institutions and they have no comprehensive subject index. Hence they have been inaccessible and difficult to use. This project, with the support of academics, archivists and politicians, has taken the Papers and fully digitised them. The resource has been available online since October 2006.

Visitors to the site can search either the full text or specific keywords (for example Prisons, Westminster or Drunkenness), or they can browse particular debates according to the combined subject index, or they can simply view the volumes.

Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model

We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art

An Evidential Approach to Query Interface Matching on the Deep Web

Matching query interfaces is a crucial step in data integration across multiple Web databases. The problem is closely related to schema matching that typically exploits different features of schemas. Relying on a particular feature of schemas is not suffcient. We propose an evidential approach to combining multiple matchers using Dempster-Shafer theory of evidence. First, our approach views the match results of an individual matcher as a source of evidence that provides a level of confidence on the validity of each candidate attribute correspondence. Second, it combines multiple sources of evidence to get a combined mass function that represents the overall level of confidence, taking into account the match results of different matchers. Our combination mechanism does not require use of weighing parameters, hence no setting and tuning of them is needed. Third, it selects the top k attribute correspondences of each source attribute from the target schema based on the combined mass function. Finally it uses some heuristics to resolve any conflicts between the attribute correspondences of different source attributes. Our experimental results show that our approach is highly accurate and effective.