Feature Management for Efficient Camera Tracking
Harald Wuest, Alain Pagani, Didier Stricker
Asian Conference on Computer Vision 8 Asian Conference on Computer Vision (ACCV-2007), located at 8th, November 18-22, Tokyo, Japan

Abstract:

In dynamic scenes with occluding objects many features need to be tracked for a robust real-time camera pose estimation. An open problem is that tracking too many features has a negative e ffect on the real-time capability of a tracking approach. This paper proposes a method for the feature management, which performs a statistical analysis of the ability to track a feature and then uses only those features which are very likely to be tracked from a current camera position. Thereby a large set of features in diff erent scales is created, where every feature holds a probability distribution of camera positions from which the feature can be tracked successfully. As only the feature points with the highest probability are used in the tracking step, the method can handle a large amount of features in diff erent scale without losing the ability of real time performance. Both the statistical analysis and the reconstruction of the features' 3D coordinates are performed online during the tracking and no preprocessing step is needed.

Feature Management for Efficient Camera Tracking
Harald Wuest, Alain Pagani, Didier Stricker
Asian Conference on Computer Vision 8 Asian Conference on Computer Vision (ACCV-2007), located at 8th, November 18-22, Tokyo, Japan

Abstract:

In dynamic scenes with occluding objects many features need to be tracked for a robust real-time camera pose estimation. An open problem is that tracking too many features has a negative e ffect on the real-time capability of a tracking approach. This paper proposes a method for the feature management, which performs a statistical analysis of the ability to track a feature and then uses only those features which are very likely to be tracked from a current camera position. Thereby a large set of features in diff erent scales is created, where every feature holds a probability distribution of camera positions from which the feature can be tracked successfully. As only the feature points with the highest probability are used in the tracking step, the method can handle a large amount of features in diff erent scale without losing the ability of real time performance. Both the statistical analysis and the reconstruction of the features' 3D coordinates are performed online during the tracking and no preprocessing step is needed.