The road accidents take place frequently which causes huge loss of life and property because of the poor emergency facilities. Our project will provide an optimum solution to this draw back. This design is a system which can detect accidents in significantly less time and sends the basic information to various authorities within a few seconds covering geographical coordinates in which a vehicle accident had occurred. This alert message is sent to the rescue team in a short time, which will help in saving the valuable lives.
LITERATURESURVEY
In this paper, an algorithm is proposed to evaluate the speed of the accident vehicle by videos. With human interaction, control points are refined by corner detector and then used for two-dimension geometric correction. The frame difference method is employed to capture the accident vehicle in images. Once the velocity curve is obtained and smoothed, the motion trajectory and speed of an accident vehicle at collision can be captured as well. Experimental results show that the proposed method is more accurate in speed measurement. It will facilitate the traffic police to deal with traffic collision cases in a real world. Although the dynamic approach has been widely applied now and the velocity of the accident vehicle can be calculated by comprehensive analysis on the vehicle and trace on site, it is still difficult to obtain a reasonable and accurate result through the dynamic approach since some parameters are uncertain and the trace on road surface is difficult to retain, measure accurately or being repeated. Although the dynamic approach has been widely applied now and the velocity of the accident vehicle can be calculated by comprehensive analysis on the vehicle and trace on site, it is still difficult to obtain a reasonable and accurate result through the dynamic approach since some parameters are uncertain and the trace on road surface is difficult to retain, measure accurately or being repeated. The proposed system helps to measure the accelerating speed. Here no measures are taken for the accident [1].
Intelligent visual surveillance for road vehicles is the key to developing autonomous intelligent traffic systems. Traffic incident detection employing computer vision and image processing has attracted much attention. In this paper, a probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3-D model-based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Finally, vehicle activity is predicted by locating and matching each partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. TRAFFIC is of great importance in a modern society. The effective management of traffic, especially of road vehicles, has become an urgent problem to be solved. Traffic surveillance using monitoring cameras has already been widely applied in current traffic management. However, current methods depend on human observation of captured video sequences of images. This requires a great deal of human work and does not allow a real-time response to abnormal events. With computer vision and image-processing methods, intelligent traffic surveillance systems. A 3-D model based vehicle tracking is used for efficient tracking. But here only detection of accident will be shown, there is no prevention or alerting system [2].