The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately from feature extraction, and update these filters using a moving average operation with an empirical weight. These DCF trackers hardly benefit from...
Accurately tracking the six degree-of-freedom pose of an object in real scenes is an important task in computer vision and augmented reality with numerous applications. Although a variety of algorithms for this task have been proposed, it remains difficult to evaluate existing methods in the literature as oftentimes different sequences are used and no large benchmark datasets close to realworld scenarios...
In this paper, we propose a multi-task correlation particle filter (MCPF) for robust visual tracking. We first present the multi-task correlation filter (MCF) that takes the interdependencies among different features into account to learn correlation filters jointly. The proposed MCPF is designed to exploit and complement the strength of a MCF and a particle filter. Compared with existing tracking...
In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module,...
Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the target and the background, and hence may fail with more possibility when there is...
We propose an online algorithm based on local sparse representation for robust object tracking. Local image patches of a target object are represented by their sparse codes with an over-complete dictionary constructed online, and a classifier is learned to discriminate the target from the background. To alleviate the visual drift problem often encountered in object tracking, a two-stage algorithm...
While numerous algorithms have been proposed for object tracking with demonstrated success, it remains a challenging problem for a tracker to handle large change in scale, motion, shape deformation with occlusion. One of the main reasons is the lack of effective image representation to account for appearance variation. Most trackers use high-level appearance structure or low-level cues for representing...
We propose an online tracking algorithm in which the support instances are selected adaptively within the multiple instance learning framework. The support instances are selected from training 1-norm support vector machines in a feature space, thereby learning large margin classifiers for visual tracking. An algorithm is presented to update the support instances by taking image data obtained previously...
We propose an algorithm for accurate tracking of (articulated) objects using online update of appearance and shape. The challenge here is to model foreground appearance with histograms in a way that is both efficient and accurate. In this algorithm, the constantly changing foreground shape is modeled as a small number of rectangular blocks, whose positions within the tracking window are adaptively...
We present a dynamic inference algorithm in a globally parameterized nonlinear manifold and demonstrate it on the problem of visual tracking. An appearance manifold is usually nonlinear, embedded in a high dimensional space, and can be approximated by a mixture of locally linear models. Existing methods for nonlinear dimensionality reduction, which map an appearance manifold to a single low dimensional...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.