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.
In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes. The goal of tracking is to search the unlabeled sample that is the most relevant to the existing labeled nodes. Therefore, visual tracking is regarded as a ranking problem in which the relevance...
It is a challenging task to develop an effective and robust visual tracking method due to factors such as pose variation, illumination change, occlusion, and motion blur. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. We first compute the discriminative weights by sparse construction error with template dictionary consisted of positive and negative...
In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. For tracking, tracked results are taken as labeled nodes while candidate samples are taken as unlabeled nodes, and the goal of tracking is to search the unlabeled sample that is the most relevant with existing labeled nodes by manifold ranking algorithm. Meanwhile, we adopt non-adaptive random projections...
The performance of visual tracking mainly depends on observation models and search methods. To stabilize the tracker, an algorithm for estimating velocity feature based on principal component analysis (PCA) is proposed. The proposed algorithm calculates the velocity feature using PCA by the states of the object in the previous k frames. In addition, integrating color and motion cues for visual tracking...
The particle filter is a popular tool for visual tracking. Traditionally, the number of particles used is typically fixed, and the motion model is simply a random walk with fixed noise variance. All these factors make the visual tracker unstable. To stabilize the tracker and guarantee the real-time tracking, an adaptive particle filter algorithm which estimates the motion model parameters using principal...
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.