International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-1, November 2019 887 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: A4419119119/2019©BEIESP DOI: 10.35940/ijitee.A4419.119119 Abstract: Object tracking is a troublesome undertaking and significant extent in data processor perception and image handling community. Some of the applications are protection surveillance, traffic monitoring on roads, offense detection and medical imaging. In this paper a recent technique for tracking of moving object is intended. Optical flow information authorizes us to know the displacement and speed of objects personate in a scene. Apply optical flow to the image gives flow vectors of the points to distinguishing the moving aspects. Optical flow is accomplished by Lucas canade algorithm. This algorithm is superior to other algorithms. The outcomes reveals that the intend algorithm is efficient and accurate object tracking method. This paper depicts a smoothing algorithm to track the moving object of both single and multiple objects in real time. The main issue of high computational time is greatly reduced in this proposed work. Keywords: Computer Vision, Lucas Canade Algorithm, Object Tracking Optical Flow Technique. I. INTRODUCTION Moving target tracking is to determine locations that the same object from different frames in image sequences. When moving object is correctly detected, the tracking is equivalent to the characteristics matching problem. The characteristics are created in consecutive image frames based on location, velocity, shape, texture, color and so on. Moving object tracking is a very considerable subject in the extent of data processor vision. It is the premise of the object detection and the cognizant behavior analysis. The robust object detection is a necessary undertake for the precision of subsequent object tracking and comportment analysis. Many object detection algorithms have been intended, such as temporal differencing, frame difference method background subtraction method, optical flow method and so on. Temporal differencing [1] taken into account dissimilarity in two succeeding frames with esteem to time. It is primarily same as background subtraction technique to subtract the image fetters target information through threshold value. It is very quiet to implement but it is lack of significant pixels of some types of moving object and cannot used in real time implementations. Revised Manuscript Received on November 06, 2019. C.Karthika Pragadeeswari, Assistant Professor, Department of ECE, Alagappa Chettiar Government college of Engineering and Technology, Karaikudi 630003 India. Email: bk.karthika1969@gmail.com G.Yamuna, Professor and Head, Department of ECE, Annamalai University, Annamalainagar, 608002 India. Email: yamuna.sky@gmail.com G.Yasmin Beham, PG Scholar, Department of ECE, Alagappa Chettiar Government college of Engineering and Technology, Karaikudi 630003 India. Email: yasar14051996@gmail.com Frame difference method [2] is a simple algorithm, in that dispute between two successive frames obtained and the object can be recognized by this method and is tracked by template matching algorithm. This system is cost effective and utilizes a supervision tool in distinct applications. Background subtraction method [3] is used to subtract the current frame from background taken as reference and various background subtraction models are analyzed and compared with their capability, reminiscence and fidelity. It is the prime method for extended applications. Optical flow technique characterized as the changes in the movement of object with respect to brightness pattern in image. Optical flow computes the motion between two frames for every pixel in the frame which are taken at different time intervals. Optical flow is extensively used technique to track the moving objects efficiently. This method is capable of providing complete movement information and detects the moving object from the background better than the other methods. However this method is useful in object tracking. The main work of target tracking is to select good target characteristics. Determining an optical flow [4] is quiet important. A technique for evaluating the optical flow instance is demonstrated which appropriates that alters in location analogous to brightness pattern. It readily varies at any place in the image. A repeating performance is denoted which satisfyingly projects the optical flow for several criterions in image sequences. The algorithm is incredibly rapid and additionally unfeeling towards quantization of contrast levels and additive noise. Prediction and Tracking of Moving Objects in Image Sequences [5] that we utilize a forecast model for movement of object velocity and area estimation got from Bayesian hypothesis. Segmentation and optical flow tracking is used for predicting future frames. The proposed algorithm provides good moving object tracking capabilities. Such capabilities are used for segmenting and estimating the moving object velocity and segmentation in a future frame. Optical Flow Based Moving Object Detection and Tracking for Traffic Surveillance [6] that trusting on optical flow can be through Horn Schunck algorithm. To evacuate clamors, median filter is utilized and the undesirable objects are excluded by applying Thresholding calculations in morphological tasks. The outcomes prove that the framework powerfully recognizes and tracks moving aspects in urban video and evacuates the undesirable objects which are not vehicles. A Robust Algorithm for Real Time Tracking With Optical Flow C. Karthika Pragadeeswari, G.Yamuna, G. Yasmin Beham