In this paper, a process is described for analysing the motion of a human target in a video stream. Moving targets are detected and their boundaries extracted. From these, a "star" skeleton is produced. Two motion cues are determined from this skeletonization: body posture, and cyclic motion of skeleton segments. These cues are used to determine human activities such as walking or running, and even potentially, the target's gait. Unlike other methods, this does not require an a priori human model, or a large number of "pixels on target". Furthermore, it is computationally inexpensive, and thus ideal for real-world video applications such as outdoor video surveillance.

This paper describes a method for detecting multiple overlapping objects from a real-time video stream. Layered detection is based on two processes: pixel analysis and region analysis. Pixel analysis determines whether a pixel is stationary or transient by observing its intensity over time. Region analysis detects stationary regions of stationary pixels corresponding to stopped objects. These regions are registered as layers on the background image, and thus new moving objects passing through these layers can be detected. An important aspect of this work derives from the observation that legitimately moving objects in a scene tend to cause much faster intensity transitions than changes due to lighting, meteorological, and diurnal effects. The resulting system robustly detects objects at an outdoor surveillance site. For 8 hours of video evaluation, a detection rate of 92% was measured, which is higher than traditional background subtraction methods.

A new depth measurement method is described in this paper. This method uses the parallel planes of light each of which flickers in the time domain according to the binary code uniquely assigned to it. When the code length is n bits, we input n pictures projected on a certain object in which we locate and "identify" each slit image. Then the depth to the points on the slit images can be calculated by triangulation. The experimental results show that this method is faster and stronger for noise than the conventional methods. We aim to investigate the importance of knowledge about the task domain being used in signal level processing. Picture processing systems are usually task dependent, so the knowledge about the domain can be applied even in signal level processing. The knowledge is, we believe, more powerful to be used at earlier stage in picture processing than to be used at the latter one. A result about the transformation from knowledge to signal-to-noise ratio is shown as an example.

We present a method for estimating activities of multiple, interacting objects detected by a video surveillance system. The activities are described in a stochastic context because our method is concerned with humans and uses noisy features detected from video. To monitor activities in this context, we introduce the concept of an attribute set for each blob, consisting of object type, action, and interaction. Using probabilistic relations introduced by a specific Markov model of these attribute sets, the activity descriptions are estimated from surveillance video.

Recently, researches and developments for measuring and modeling of the human body have been receiving much attention. Our aim is to reconstruct an accurate shape of a human foot from multiple camera images, which can capture dynamic behavior of the object. In this paper, a foot-shape database is used for accurate reconstruction of human foot. By using Principal Component Analysis, the foot shape can be represented with new meaningful variables. The dimensionality of the data is also reduced. Thus, the shape of object can be recovered efficiently, even though the object is partially occluded in some input views. To demonstrate the proposed method, two kinds of experiments are presented: reconstruction of human foot in a virtual reality environment with CG multi-camera images, and in real world with eight CCD cameras. In the experiments, the reconstructed shape error with our method is around 2 mm in average, while the error is more than 4 mm with conventional volume intersection method.

In the field of computer vision and computer graphics, Image-Based-Rendering (IBR) methods are often used to synthesize images from real scene. The image synthesis by IBR requires dense correct matching points in the images. However, IBR does not require 3D geometry reconstruction or camera calibration in Euclidean geometry. On the other hand, 3D reconstructed model can easily point out the occlusion in images. In this paper, we propose an approach to reconstruct 3D shape in a voxel space, which is named Projective Voxel Space (PVS). Since PVS is defined by projective geometry, it requires only weak calibration. PVS is determined by rectifications of the epipolar lines in three images. Three rectified images are orthogonal projected images of a scene in PVS, so processing about image projection is easy in PVS. In both PVS and Euclidean geometry, a point in an image is on a projection from a point on a surface of the object in the scene. Then the other image might have a correct matching point without occlusion, or no matching point because of occlusion. This is a kind of restriction about searching matching points or surface of the object. Taking advantage of simplicity of projection in PVS, the correlation values of points in images are computed, and the values are iteratively refined using the restriction described above. Finally, the shapes of the objects in the scene are acquired in PVS. The reconstructed shape in PVS does not have similarity to 3D shape in Euclidean geometry. However, it denotes consistent matching points in three images, and also indicates the existence of occluded points. Therefore, the reconstructed shape in PVS is sufficient for image synthesis by IBR.