Abstract. Every year, tragedy of indoor emergency accidents lead to immeasurable loss to families, bringing the society great shocks. This is mainly due to the harsh environment of emergency accidents, like firefighting, as the on-fire buildings are usually full of smoke and danger. One of the most directional issues is localization, as it is almost impossible to find the person trapped in the building without knowing the location. Localization is not only critical for evacuees seeking help from outside, but also important for self-evacuation. But unfortunately, a reliable and applicable indoor localization system is not available today, because of accuracy, cost and efficiency. In this paper, we present a novel localization system, which combines pedestrian reckon algorithm with wireless sensor network, hence providing an automatic and reliable solution towards this issue.

Abstract. As the traffic jam is becoming a serious issue of urban life, collecting traffic parameters for traffic distribution is important and critical. In this paper, we present a camera based system (Traf) to count automotive vehicles for traffic flow analysis use. The system employs the method of background subtraction by comparing differences between foreground and background. The subtraction result is utilized to identify the position and the shape of vehicles. The background model is initialed by averaging first 50 frames, and adapted to environment change by introducing an adapting parameter λ. Compared to state-of-art method, Traf is more robust, adaptive and economical.