Security has become the most essential aspect in every walk of life. With ever growing technology, there has been an apparent inclination towards developing smart surveillance systems that do not stop with just monitoring and recording of events, but also possesses the ability to observe the events and alert in case of any discrepancies if observed. One of the impending threats to security in crowded places is the ignorance of un-attended objects. These un-attended objects or abandoned objects may have been left behind intentionally and may contain hazardous things which can cause huge disasters. This paper is focused towards developing a computer vision based approach that analyses the blob areas to detect any abandoned objects and instantaneously send appropriate alert without any human intervention. The uniqueness of this approach is that it handles the occlusion scenario and also quick execution time to detect abandoned objects. The approach has been tested with various benchmark video datasets and real-time video sequences as well. The performance has been measured in terms of accuracy of classifying abandoned objects in a given video sequence and also the execution time taken for computing the outcome. Results indicate good accuracy in terms of abandoned object detection under varying conditions and scenarios and also faster execution time when compared to other contemporary approaches.