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.
Major problem of tracking a visual target in occurrence of out-of-field-of-view is unable to obtain information of the target from image frame. We solve this problem by utilizing background information in the image frame. Scenes of tracking a car from an observer in aerial vehicle have been employed. Background information is obtained from road area, building area, and the others. Prior probabilities...
This paper is a new attempt to introduce a ldquomulti-cue rdquo particle filter tracking method. It uses multiple features to facilitate 2D video tracking from a monocular view. The methodpsilas benefits lie in its robustness and speed. Speed is improved by adaptive adjustment of different cues; robustness is implemented by multiple cues. The proposed method is demonstrated on man tracking with a...
In this paper, we describe a new approach to improve the video based object tracking system with particle filter using shape similarity. It deals with single object tracking whose dynamics age highly non-linear. The shape similarity between a template and estimated regions in the video sequences can be measured by their normalized cross-correlation of distance transformation. Here within this present...
This paper presents a tracking algorithm based on a sequential importance sampling (SIS) particle filter scheme followed by a resampling strategy where shape and color cues are exploited to handle deformable objects. The state vector is composed by a set of corners and it enables to jointly describe position and shape of the target. Mean Shift trackers, applied to color cues associated to state subspaces,...
Robust and real time moving object tracking is a tricky job in computer vision problems. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this paper, we first try to develop a color based particle filter. In this approach, the object tracking system relies on the deterministic search of window, whose color content matches a reference histogram...
Robust and real-time object tracking of any objects is a challenging task. Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. This paper describes a new approach to improve the moving object tracking system with particle filter using shape similarity. The shape similarity between a template and estimated regions in the video sequences can be measured...
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.