In video surveillance applications, the main aim will be identification of the pedestrians i.e., identifying the position of the persons in each frame of the video. In real world, many tracking tasks suffer from the multimodal likelihood and posterior and inaccurate local evidence. The abrupt movements and scaling of the objects in the video always remains a problem for tracking process. The objects can be tracked using many methodologies such as object based tracking and position based tracking. The object based tracking methods identifies the objects in the video using background subtraction and tracks the objects. The object based methods cannot be able to handle occlusions. The position based tracking methods tracks the objects in the video based on the initial position of the target and updating the position of the target by learning of the position of the target in the consequent frames. The input video is converted into frames. The frames were preprocessed to remove the noises from the frames. The objects are detected from the frames by initially identifying the object regions by applying Background subtraction. The thresholding operation and morphological operations were applied to extract the object alone. The measurements of the regions that were identified as objects were calculated and based on the calculated values the objects in the video is tracked by plotting a rectangle around the object. The obtained object pixels were then grouped and the identified positions were then represented in different color in order to obtain them exactly. Finally the performance of the process is measured by measuring the error rate and accuracy. The measure performance of the process shows that the proposed method is capable for the identification of the object positions in accurate manner. In our proposed the object track one to one and one to two methods detect the object in very accurate manner. That objects track is very accurate in video survey line cameras.