For understanding human activities in urban public spaces, we have been developing a sensing platform for visualizing data derived from sensor devices in the spaces. The platform collects sensing data from sensor devices installed in public spaces, and provide a simple communication interface to access the sensor data for client systems. In this demo abstract, we describe a working prototype of a sensor data streaming service for visualizing urban public spaces. The streaming data includes the number of pedestrians, pedestrian flows, and sound pressures of the human activities in the sensing area. As an application of the streaming service, we will show a client system that visualizes the current pedestrian flows in the sensing area with very little delay. The flows are visualized like a view of live camera without pedestrians' appearances. The system has a search interface to retrieve the sensing data in a past period and also visualizes them.

In this paper we propose a very efficient and low-cost method for real-time 3D object pose estimation, suitable for operation under severe changes in the illumination conditions. The method consists of a geometry-based and learning-based stage: first a geometry-based pose estimation algorithm is used to obtain pose parameter values, which are utilized as supervised information during a following learning-based stage. The efficacy of the proposed approach is illustrated on a real-world application in a head pose based facial-gesture interface for automatic wheelchair navigation.

In facility management, analysis of customer trajectories in office complexes is considered critical. In this paper, we propose a novel approach for the visualization of customer flow in an office complex over a long period of time. We expressed the variation in the trajectories with respect to time by using a mixture model; this was used for the visualization of the trajectory flows. The effectiveness of our approach was evaluated from the results of the customer flow analysis experiments that were conducted in an office complex.

We have developed a head gesture controlled electric wheelchair system to aid persons with severe disabilities. Realtime range information obtained from a stereo camera is used to locate and segment the face images of the user from the sensed video. We use an Isomap based nonlinear manifold learning map of facial textures for head pose estimation. Our system is a non-contact vision system, making it much more convenient to use. The user is only required to gesture his/her head to command the wheelchair. To overcome problems with a non responding system, it is necessary to notify the user of the exact system state while the system is in use. In this paper, we explore the use of vibrotactile rendering of head gestures as feedback. Three different feedback systems are developed and tested, audio stimuli, vibrotactile stimuli and audio plus vibrotactile stimuli. We have performed user tests to study the usability of these three display methods. The usability studies show that the method using both audio plus vibrotactile response outperforms the other methods (i.e. audio stimuli, vibrotactile stimuli response).

We propose a method of identifying persons who are using wheelchairs in real environments on the basis of HOG features extracted from disparity images. First, we use USV, which is a stereo vision system for detecting humans, to determine the 3-D location of the humans moving within the monitored area, and cut out the corresponding regions in the disparity image. Then, we compute a HOG feature vector from those cut-out images, divide the results into the two classes of persons in wheelchairs and pedestrians and perform training and distinguishing by SVM. The data dealt with here involved people moving around without restriction in real environments. Also, there was no guarantee that the photographed environment was the same at the time of training data acquisition and at the time of recognition processing. We conducted training and recognition experiments on this method using video data acquired in the laboratory that confirmed a per-frame recognition rate of 99% or higher. Furthermore, after training with the same laboratory data and application to video data of visitors to an assistive products exhibition, a per-frame correct recognition rate of 80% was confirmed.

Dozens of people are killed every year at railroad crossings, a situation that requires urgent action by railroad companies, especially in major cities. We are therefore developing a ubiquitous stereo vision based system for ensuring safety at railroad crossings. In this system, stereo cameras are installed at four corners and are pointed toward the center of the railroad crossing to monitor the passage of people. The system determines automatically and in real-time whether anyone or anything is inside the railroad crossing, and whether anyone remains in the crossing. The system can be configured to automatically switch over to a surveillance monitor or automatically connect to an emergency brake system in the event of trouble.

To attain smooth human interaction we propose a system which simultaneously utilizes the stereo disparity and optical flow information of real-time stereo gray and multi resolution images to recognize objects and gestures. For real-time calculation of the disparity and optical flow information of a stereo image the system first creates pyramid images by utilizing a Gaussian filter. The system then determines the disparity and optical flow of a low density image and extracts regions in front of a certain depth. The three foremost regions are recognized by higher order local autocorrelation features and a linear discriminant analysis. With this process the system recognizes the face and hand signs of users which are displayed foremost, and roughly recognizes movements within the region in real-time. With this framework, the system can discriminate, the face of a user, can monitor the basic movements of the user, can smoothly learn a presented object by users and can communicate with users from hand signs learned in advance.

This paper presents a method to obtain an image processing procedure by showing an original image and a target image. There are some preceding works concerns with automatic acquisition of image processing procedures by combining subroutine packages. In our approach, we only use the hierarchical mathematical morphology procedures, i.e., erosion and dilation operations. Since the subroutine packages are macro procedures, we use micro operations for constructing more flexible visual procedures. We use genetic algorithms (GA) to search procedures. Utilizing a parallel searching function of GA and introducing a concept of era, we construct hierarchical procedures of mathematical morphology.