The JavaScript programming language, originally developed as a simple scripting language, is now the language of choice for web applications. All the top 100 sites on the web use JavaScript and its use outside web pages is rapidly growing. However, JavaScript is not yet ready for programming in the large: it does not support a module system. Lack of namespaces introduces module patterns, and makes it difficult to use multiple JavaScript frameworks together.

In this paper, we present a walking guidance system for the visually impaired pedestrians. The system has been designed to help the visually impaired by responding intelligently to various situations that can occur in unrestricted natural outdoor environments when walking and finding the destinations. It involves the main functions of people detection, text recognition, face recognition. In addition, added sophisticated functions of walking path guidance using Differential Global Positioning System, obstacle detection using a stereo camera and voice user, interface are included. In order to operate all functions concurrently, we develop approaches in real situations and integrate them. Finally, we experiment on a prototype system under natural environments in order to verify our approaches. The results show that our approaches are applicable to real situations.

We present a method for detecting and tracking multiple people totally occluded or out of sight in a scene for some period of time in image sequences. Our approach is to use time weighted color information (i.e. the temporal color) for robust medium-term people tracking. The temporal color is the set of pairs of a color value and its associated weights. The weight is related to the size, duration and frequency of appearance of the color region, as well as the number of people adjacent to the target person. It assures our system to continuously track people moving in a group with occlusion. Most systems have built an appearance model for each person to solve occlusion problems. The appearance model contains certain information on the target person — color, shape, texture, position, velocity and face pattern. We use temporal color in the appearance model for the identification of the people occluded or out of sight in the scene upon their reappearance. Experimental results show that the temporal color is more stable than shape or intensity in various cases.

In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method.

We present a method for the detection and tracking of multiple people totally occluded or out of sight in a scene for some period of time in image sequences. Our approach is to use time weighted color information, i.e., the temporal color, for robust medium-term people tracking. It assures our system continuously tracks people moving in a group with occlusion. Experimental results show that the temporal color is more stable than shape or intensity when used in various cases.