Intelligent Transportation System Analysis for Shadow Maimed Traffic Surveillance Visionary

Video surveillance is the hot research topic under Intelligent Transport System which is useful to trace a particular missing vehicle, to detect the cause of an accident and also to find out shortest path between major places. In such areas object detection and shadow elimination is the challenging task. Shadow elimination allows enhancing the picture quality and also helps in the process of object detection via edge detection. Object detection in the field of computer vision stands to be a crucial and critical task for object and scene recognition, employment of object recognition is vast in field of surveillance and artificial intelligence. This paper proposes an Inner –Outer Outline Profile (IOOPL) algorithm to detect the three levels of object boundaries to enhance the satellite imagery segmentation with the help of shadow elevations. This method can be adapted to an experimental model of traffic surveillance video monitoring. In Intelligent Transportation System (ITS) it is very important to recognize the type of a detected object in order to track reliably and estimate traffic parameters correctly .This work addresses the major difficulty faced in the vehicle image segmentation due to the misconception of assuming object shadows as part of object itself. The proposed methodology will be utilized for vehicle detection and segmentation by eliminating its shadow counterpart. Additionally, a method for log generation to retrieve vehicle’s travelling details, is also to be proposed as a future work by classifying the vehicles using the machine learning technique, Artificial Neural Network, which is to be trained by a highperformance machine learning algorithm called Delta Learning Algorithm.