Evaluation of Object Tracking System using Open-CV In Python

Abstract

Object Tracking System used to track the motion trajectory of an object in a video. First, I use the OpenCV's function, select ROI, to select an object on a frame and track its motion using built-in-tracker. Next, Instead of using selectROI, I use YOLO to detect an object in each frame and track them by object centroid and size comparison. Then I combine YOLO detection with the OpenCV's built in tracker by detecting the object in the first frame using YOLO and tracking them using selectROI. The video tracking is widely used in multiple purpose such as: human-computer interaction, security and surveivallence, traffic control, medical imaging, and so on. INTRODUCTION Object tracking is a very challenging task in the presence of variability Illumination condition, background motion, complex object shape partial and full object occlusions. Object detection and location in digital images has become one of the most important applications for industries to ease user, save time and to achieve parallelism. This is not a new technique but improvement in object detection is still required in order to achieve the targeted objective more efficiently and accurately. The main aim of studying and researching computer vision is to simulate the behavior and manner of human eyes directly by using a computer and later on develop a system that reduces human efforts shows the basic block diagram of detection and tracking. In this paper, an SSD and Mobile Nets based algorithms are implemented for detection and tracking in python environment. Object detection involves detecting region of interest of object from given class of image. Different methods are-Frame differencing, Optical flow, Background subtraction. This is a method of detecting and locating an object which is in motion with the help of a camera. Detection and tracking algorithms are described by extracting the features of image and video for security applications. Features are extracted using CNN and deep learning. Classifiers are used for image classification and counting. YOLO based algorithm with GMM model by using the concepts of deep learning will give good accuracy for feature extraction and classification. OBJECTIVES: Object tracking system aims to improve performance of object detection and tracking by contributing originally to two components 1) motion segmentation 2) object tracking Therefore the main objectives are: • To identify the targeted object in moving sequence • To analyze YOLO based algorithm with GMM model to get good accuracy for feature extraction and classification • To analyze the motion of the object in a video using OpenCV • To analyze SSD and Mobile Nets algorithm for tracking the objects