Object tracking is a well-studied problem in computer vision and has many practical applications. The problem and its difficulty depend upon several factors such as the knowledge about the target object, its quantity and type of parameters being tracked. Although there has been some success with building trackers for specific object classes such as human, face, mice etc. Tracking generic objects has remained challenging issue because an object can drastically change its appearance when deforming, rotating out of plane or when the illumination of the scene changes. Especially in the videos which are not clear in its original form i.e. the video which requires enhancement and its quality has to be improved. Here in the proposed work different algorithms are carried out to detect the generic objects such as the non-rigid objects and the deformable objects which are in occlusion, (i.e. in a cluttered environment) not only in the images which contain some sort of objects but also from the images which contains numerous objects which are unseen so as to improve the tracking efficiency. By doing this, the objects which are non-rigid and changing in nature can also be predicted in a perfect manner so that the computational complexity can be reduced, reliability, accuracy, and efficiency can be improved.