A novel pedestrian tracking scheme based on a particle filter is proposed, which adopts a skeleton model of a pedestrian for a state space model and distance transformed images for likelihood computation. The 6-stick skeleton model used in the proposed approach is very distinctive in representing a pedestrian simply but effectively. By the experiment using the real sequences provided by PETS, it is shown that the target pedestrian is tracked adequately by the proposed approach with a simple silhouette extraction method which consists of only background subtraction, even if the tracking target moves so complicatedly and is often so cluttered by other obstacles that the pedestrian can not be tracked by the conventional methods. Moreover, it is demonstrated that the proposed scheme can track the multiple targets in the complex case that their trajectories intersect.

In this paper, efficient algorithms for content-based video retrieval using motion information are proposed. We describe algorithms for temporal scale invariant retrieval using Distance transformation and temporal scale absolute retrieval using Motion Retrieval Code. The effectiveness of the proposed algorithms has been verified by experimental results.

The skeleton and the skeleton function of an object are important representations for shape analysis and recognition. They contain enough information to recognize an object and to reconstruct its original shape. However, they are sensitive to distortion caused by rotation and noise. This paper presents another approach for binary object representation called a modified exoskeleton(mES) that combines the previously defined exoskeleton with the use of symmetric object whose dominant property is rotation invariant. The mES is the skeleton of a circular background around the object that preserves the skeleton properties including significant information about the object for use in object recognition. Then the matching algorithm for object recognition based on the mES is presented. We applied the matching algorithm to evaluate the mES against the skeleton obtained from using 4-neighbor distance transformation on a set of artificial objects, and the experimental results reveal that the mES is more robust to distortion caused by rotation and noise than the skeleton and that the matching algorithm is capable of recognizing objects effectively regardless of their size and orientation.

In this paper, we present new algorithms to calculate the reverse distance transformation and to extract the skeleton based upon the Euclidean metric for an arbitrary binary picture. The presented algorithms are applicable to an arbitrary picture in all of n-dimensional spaces (n2) and a digitized picture sampled with the different sampling interval in each coordinate axis. The reconstruction algorithm presented in this paper is resolved to serial one-dimensional operations and efficiently executed by general purpose computer. The memory requirement is very small including only one picture array and single one-dimensional work space array for n-dimensional pictures. We introduce two different definitions of skeletons, both of them allow us to reconstruct the original binary picture exactly, and present algorithms to extract those skeltons from the result of the squared Euclidean distance transformation.