Video Object Motion Tracking using Dense Optical Flow Techniques

Abstract

Digital video content refers to video content that is stored and transmitted in a digital format. Video contains the Objects and their motion. Object detection and motion tracking are two of the most fundamental tasks in digital video content analysis. The main goal of this paper is to detect multiple objects and track their motion in a dynamic environment. This paper provides motion tracking of multiple objects using dense optical flow approaches such as FarneBack method and FarneBack-Gaussian method. Video datasets used for the experiment are collected from YouTube. The experiment result shows that FarneBack-Gaussian dense optical flow approach provides higher precision, stability and computationally efficient than FarneBack dense optical flow. Various mechanisms like accuracy and time complexity are used to test and validate results. The significance of this research is to extract information about the motion and behavior of objects within video in a dynamic environment. Security companies, sports organizations, autonomous vehicle manufacturers, government agencies and academic researchers will benefit from this research.