The purpose of prosecution is segmentation feature a region of interest in a video scene and retention proposal, positioning and locking. Object detection and classification of objects are preceded steps for tracking an object in the image sequence. Object detection is done to check for objects in the video and find out exactly what the object. Then the detected object can be classified into different categories, such as people, vehicles, birds, clouds floating, swaying trees and other moving objects. Object Tracking is performed using spatial and temporal changes of items during a video monitoring, including presence, position, size, shape, etc. The object is used in several applications such as video surveillance, robot vision, monitoring traffic Video and Animation painting. This paper presents a brief overview of object detection, object classification and object tracking different algorithms available in the literature, including the analysis and comparative study of the various techniques used at various stages of prosecution.

Here we are giving a novel framework for ThreeD face regetting from an single depth image is proposed. We make use of an inexpensive Kinect® sensor which provides a depth map of the subject. The depth map obtained is of low resolution having substantial random noise and corruption which makes the 3D face reconstruction problem more difficult to implement. We introduce a novel algorithm that effectively maps the low resolution depth maps to realistic face models. We extract the sparse errors from the depth images using a data-driven local sparse coding technique and reconstruct the entire shape by combining local shapes using a template based method. Our approach is able to produce high-resolution 3D face models with better accuracy.