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As long as visual features for recognition are known in advance and remain static due to a controlled environment, object tracking is state-of-the-art. Tracking objects in dynamically changing environments is still a challenge. Even harder is the tracking of moving objects with a moving camera. Our algorithm realizes a deterministic approach to track any 2D-features representable in a general way...
We propose a novel inertial-aided KLT feature tracking method robust to camera ego-motions. The conventional KLT uses images only and its working condition is inherently limited to small appearance change between images. When big optical flows are induced by a camera-ego motion, an inertial sensor attached to the camera can provide a good prediction to preserve the tracking performance. We use a low-grade...
Visual tracking with moving cameras is a challenging task. The global motion induced by the moving camera moves the target object outside the expected search area, according to the object dynamics. The typical approach is to use a registration algorithm to compensate the camera motion. However, in situations involving several moving objects, and backgrounds highly affected by the aperture problem,...
A robust approach to detection and tracking of multiple moving targets from a moving camera is presented. The main novelty of this approach is that objects are represented using efficient compact form of the colour correlogram. Like previous correlograms, this encodes both spatial pattern and appearance information about the target. However it is less complex to compute, making it applicable to real...
This paper describes an approach to tracking multiple independently moving objects observed from moving cameras. The method addresses difficulties typically associated with tracking, including changes in background, parallax in the scene, arbitrary camera motion, object occlusions, cross-overs, and appearance changes. Using a bottom up approach, independently moving objects are detected in images...
A generalized expectation maximization (GEM) algorithm is used to retrieve the pose of a person from a monocular video sequence shot with a moving camera. After embedding the set of possible poses in a low dimensional space using principal component analysis, the configuration that gives the best match to the input image is held as estimate for the current frame. This match is computed iterating GEM...
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