ABSTRACT Long-term tracking is important for maritime situational awareness to identify currently observed ships as earlier encounters. In cases of, for example, piracy and smuggling, past location and behavior analysis are useful to determine whether a ship is of interest. Furthermore

This paper addresses long term tracking of multiple objects with occlusions. Bayesian networks are used to model the interaction among the detected tracks and for conflict management, allowing the tracker to update the labelling decisions as soon as new information is available. If several objects

on the texture of the tracked object and improve invariance to lighting. Furthermore we address long-term stability, enabling the tracker to recover from drift and to provide redetection following object disappearance or oc-clusion. The two-module principle is similar to the success-ful state-of-the-art long-term

Abstract. Object tracking is a reoccurring problem in computer vision. Tracking-by-detection approaches, in particular Struck [20], have shown to be competitive in recent evaluations. However, such approaches fail in the presence of long-term occlusions as well as severe viewpoint changes

We address the problem of long-term object tracking, where the object may become occluded or leave-the-view. In this setting, we show that an accurate appearance model is considerably more effective than a strong motion model. We develop simple but effective algorithms that alternate between

following implantation of a phakic posterior chamber intraocular lens for correction of high myopia were analyzed. All EC centroids were dotted on each image, and early and late follow-up pictures were automatically aligned on the basis of these centroids. The tracking results were confirmed via alternating

Tracking an entity for a long duration allows the gathering of intelligence on a target. While the system comprises a collection of different elements (e.g., tracking, sensor tasking, etc.), the ability to track objects continuously over long periods rests on feature measurements that are collected

the reliance on the texture of the tracked object and improve invariance to lighting. Furthermore we address long-term stability, enabling the tracker to recover from drift and to provide redetection following object disappearance or occlusion. The two-module principle is similar to the successful state

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Long-term tracking of cardiovascular risk factors