International Journal of Engineering and Technology

The localization and recognition of moving objects from single monocular intensity images has been a popular issue in image analysis and computer vision over many years. It is also one of the fundamental crisis in model based vehicle localization and recognition. The recently used scheme is model based on simple object recognition and localization of road vehicles using the position and orientation of vehicle image data. But the drawback of the approach is that the shape of the vehicle and its pose varies in multiple junction coordination, the model based recognition is an inefficient one. To overcome the issues, our first work implemented a surveillance image object recognition and localization using improved local gradient model. The vehicle-object shape recognition and pose recovery in the traffic junction is carried out for varied traffic densities. But the drawback of the approach is that it considers only the vehicle shape and pose variations in the road network and does not discuss about the occurrences of event at the vehicle junction points. Now we have to focus on the process of occurrences of event like accident met at traffic junctions. For this, in this work, spatial time invariant model is introduced to measure the event occurrences of the vehicle traffic location points. The event which has been takes place is recorded as the reference context for standardization of the traffic modality. With the reference context, the detector can easily find out the reason of the event takes place. An experimental evaluation is carried out to estimate the performance of the proposed event detection at vehicle location points using spatial time invariant model (EDSTIM) in terms of spatial events, multiple time scales, traffic controlling time and compared with an existing model based on simple object recognition and localization and the previous work Surveillance of Vehicle Object Recognition and Localization.