There are countless objects present in our surrounding environment with their impressions. Vision can be better explained as a way to understand the environment that surrounds us. Even after decades, the exact working of the visual system is a mystery for the scientist involved in its investigation. When eye based vision in living creatures is replaced by computational instruments it is defined as computer vision. In other words, computer vision is artificial mimicry of vision in living creatures, where digital images and videos which are captured by cameras are further analyzed by computers obtaining an optimum level of understanding from it. Human/object tracking when done on an array of frames is an operation of tracking any mobile target object over a span of time with the help of any mobile or immobile camera. It has been a critical issue in the arena of computer vision as it is used in a number of application fields like security, surveillance, human-computer interaction, augmented reality, video communication, and compression, medical imaging, traffic control, video editing, and assistive robotics . This is a highly studied problem and remains to be a complex problem to solve. In object tracking in any given video, the major task is to trace the target object in upcoming video frames. Object tracking is a principal segment of human-computer interaction in a real-time environment, where the computer obtains a finer model of a real-time world. For example, when autonomous vehicles are talked about, a human being cannot transmit the exact state of surroundings precisely and speedily enough. The wide-ranging scope of the application review the significance of dependable, exact, and efficacious object tracking. To obtain an effective tracking the two most important parameters to be included are first, selection of the model and secondly, the tracking method worthy for the task. The fundamental necessities of any tracking structure are first, a robust system, secondly, an adaptive system, and lastly, real-time processing requirement . The famous state-of-art tracking strategies are Interest point-based tracking, multiple hypothesis tracking, kernel-based tracking, and optical flow-based tracking . This area has observed a remarkable elevation due to available low cost, advanced technology cameras, and low computing complexity, corresponding to the inclination of ingenious approaches for image and video processing. Excellent reviews on the state-of- art techniques in this area have been provided in