06-11-2012, 05:56 PM
Object Identification and Object Tracking
Object Identification and Object Tracking.doc (Size: 208 KB / Downloads: 18)
1. Introduction
Automatic detection and tracking of moving object is very important task for human computer interface video communication/expression and security and surveillance system application and so on. Various imaging techniques for detection, tracking and identification of the moving objects have been proposed by many researchers. The object detection can be divided atleast into five conventional approaches: frame difference, background subtraction, and optical flow, skin color extraction and probability based approaches. The object tracking method can be categorized into four categories: region based tracking, active contour based tracking, feature based tracking and model based tracking. The object identification is performed to evaluate the effectiveness of the tracking object especially when the object occlusion happens. It can be done by measuring the similarity between the object model and the tracked object.
In object detection methodology, many researchers have developed their methods. (Liu etal., 2001) proposed background subtraction to detect moving regions in an image by taking the difference between current and reference background image in a pixel-by-pixel. It is extremely sensitive to change in dynamic scenes derived from lighting and extraneous events etc. In another work, (Stauffer & Grimson, 1997) proposed a Gaussian mixture model based on background model to detect the object. (Lipton et al., 1998) proposed frame difference that use of the pixel-wise differences between two frame images to extract the moving regions. This method is very adaptive to dynamic environments, but generally does a poor job of extracting all the relevant pixels, e.g., there may be holes left inside moving entities.
Object detection
A subsequent action, such as tracking, analyzing the motion or identifying objects, requires an accurate extraction of the foreground objects, making oving object detection a crucial part of the system. Our object detection method consists of two main steps. The first step is pre-processing step including gray scaling, smoothing, and reducing image resolution and so on. The second step is filtering to remove the image noise contained in the object. The filtering is performed by applying the morphology filter such as dilation and erosion. And finally connected component labeling is performed on the filtered image.
Pre-processing
The first step on the moving object detection process is capturing the image information using a video camera. Image is capture by a video camera as 24 bit RGB (red, green, blue) image which each color is specified using 8-bit unsigned integers (0 through 255) that representing the intensities of each color. The size of the captured image is 320x240 pixels. This RGB image is used as input image for the next stage. In order to reduce the processing time, gray-scale image is used on entire process instead of color image. The gray-scale image only has one color channel that consists of 8 bit while RGB image has three color channels. Image smoothing is performed to reduce image noise from input image in order to achieve high accuracy for detecting the moving objects.
Object tracking
After the object detection is achieved, the problem of establishing a correspondence between
object masks in consecutive frames should arise. Indeed, initializing a track, updating it robustly and ending the track are important problems of object mask association during tracking. Obtaining the correct track information is crucial for subsequent actions, such as object identification and activity recognition.