Video tracking systems are inherently provided with non-rigid objects with various shapes and sizes, which often result
in poor match of an initial model with the actual input shape. The hierarchical approach to the active shape
model(ASM) is essential for video tracking systems to deal with such unstable inputs. The contribution of this paper is
to improve the performance of active shape model-based real-time object tracking. We also propose a hierarchical
processing framework to reduce the computational overhead. In this paper, we present a Kalman filter-based
hierarchical ASM in order to estimate a dynamic shape in video object tracking. The experimental results show that the
proposed hierarchical active shape model using Kalman filter is efficient. The experimental results also show good
results when objects are partially occluded by other objects.
This paper proposes an adaptive regularized noise smoothing algorithm for range images using the area decreasing flow method, which can preserve meaningful edges during the smoothing process. Adaptation is incorporated by adjusting the regularization parameter according to the results of surface curvature analysis. In general, range data includes mixed noise such as Gaussian or impulsive noise. Although non-adaptive version of regularized noise smoothing algorithm can easily reduce Gaussian noise, impulsive noise caused by random fluctuation of the sensor acquisition is not easy to be removed from observed range data. It is also difficult to remove noise near edge using the existing adaptive regularization algorithms. In order to cope with the problem, the second smoothness constraint is additionally incorporated into the existing regularization algorithm, which minimizes the difference between the median filtered data and the estimated data. As a result, the proposed algorithm can effectively remove the noise of dense range data while meaningful edge is well-preserved.
Multiple camera based surveillance systems provide us with a more robust tracking of objects. To take advantage of additional cameras, it is necessary to establish geometrical relationship between the cameras and relationship between an object and a camera. In recent years several techniques have been proposed, which estimate only the
relation of a dominant ground plane between different views instead of fully geometrical relation of camera-object-camera. They are however neither fully automatic nor suitable for a non-planar ground. We propose a fully automatic calibration algorithm which can cope with complex environment, including non-planar ground. The proposed algorithm automatically tracks and matches objects between different views, determines the overlapped region, and aligns each piece-wisely segmented plane between two views. The proposed calibration
algorithm minimizes the effect of occlusion and improves the accuracy of 3D measurements by using multiple views. The algorithm can also provide us large field of view by concatenating a series of cameras.