An efficient approach for object detection and tracking

Abstract

Visual surveillance System is basically used for analysis and explanation of object behaviors. It consists of static and moving object detection, video tracking to understand the events that occur in scene. The most important objective of this paper is to determine the various methods in static and moving object detection as well as tracking of moving objects. There are various classes of detected object such as tree, clouds, person and other moving objects. Detection for moving object is a very challenging for any video surveillance system. Object Tracking is used to find the area where objects are available and shape of objects in each frame in higher level application. A new proposed approach is provided for efficient object tracking using Kernel and feature based tracking methods. It is process is a Vehicle classification performance can be done in surveillance videos with the help of this method. This method requires shape and appearance of the object. Object basically contains various features and any of them is used to track object as kernel. Object tracking can be done easily if we compute the motion of the kernel of the between more than two frames. Hence dividing it into two processes are training and testing of objects in videos. First process is a trained image or frame in videos and trained object value based on shape and moving position with vehicle positive and negative results. It's store one database for testing video surveillance object values. Second process is extracted image in video after capture object value then tested in database object value, if object values are matched because result is positive then object tracked in given surveillance videos. Object matching processing use to template matching technique.