10-04-2017, 07:54 PM
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OBJECT TRACKING AND DETECTION
INTRODUCTION TO OBJECT TRACKING
Object tracking is an important task within the field of computer vision. The proliferation of high-powered computers, the availability of high quality and inexpensive video cameras, and the interesting need for automated video analysis has generated a great deal of interest in object tracking.
In its simplest form, tracking can be defined as a method of following an object through successive image frames to determine its relative movement with respect to other objects. In other words, a tracker assigns consistent labels to the tracked objects in different frames of video.
One can simplify tracking by imposing constraints on the motion or appearance of objects. One can further constrain the object motion to be of constant velocity or acceleration based on prior information. Prior knowledge about the number and the size of objects, or the object appearance and shape can also be used to simplify the problem.
Numerous approaches for object tracking have been proposed. These primarily differ from each other based on the way they approach the following question: which object representation is suitable for tracking? Which image features should be used? How should the appearance and shape of object be modelled? The answers to these questions depend on the context/environment in which the tracking is performed. A large number of tracking methods have been proposed which attempt to answer these questions for variety of scenarios.
the schematic of a generic object tracking system. As can be seen, visual input is usually achieved through digitized images obtained from a camera connected to a digital computer. This camera can be either stationary or moving depending on the application. Beyond image acquisition, the computer performs the necessary tracking and any higher-level tasks using the tracking result.OBJECT REPRESENTATION
In a tracking scenario, an object can be defined as anything that is of interest for further analysis. For instance, boats on the sea, fish inside an aquarium, vehicles on a road, planes in the air etc are a set of objects that may be important to track in a specific domain. Objects can be represented by their shapes.
In this section, we will describe the object shape representations commonly employed for tracking.
1. Points:
The object is represented by a point, that is, centroid (fig 2(a)) or by a set of points (fig 2(b)). The point representation is suitable for tracking objects that occupy small regions in an image.
2. Primitive geometric shapes:
Object shape is represented by a rectangle, ellipse (fig 2 , (d)) etc. primitive geometric shapes are more suitable for representing simple rigid objects, they are also used for tracking non rigid objects.
3. Object silhouette and contour:
Contour representation defines the boundary of an object (fig 2(g), (h)). The region inside the contour is called the silhouette of the object (fig 2(i)). Silhouette and contour representations are suitable for tracking complex non rigid shapes.
4. Articulated shape models:
Articulated objects are composed of body parts that are held together with joints. For example, the human body is an articulated object with legs, hands, head feet connected by joints. In order to represent an articulated object, one can model the constituent parts using cylinders or ellipses as shown in fig 2(e).
5. Skeletal models:
Object skeleton can be extracted by applying medial axis transform to the object silhouette. This method is commonly used as a shape representation for recognizing objects. Skeleton representation can be used to model both articulated and rigid objects (fig 2(f)).
Object representations are usually chosen according to the application domain. For tracking object, which appear very small in an image, point representation is usually appropriate. For objects whose shapes can be approximated by rectangle or ellipse, primitive geometric shape representations are more appropriate. For tracking objects with complex shapes, for example, humans, a contour or silhouette based representation is appropriate.
FEATURE SELECTION FOR TRACKING
Selecting the right features plays a critical role in tracking. The most desirable property of visual feature is its uniqueness so that the objects can be easily distinguished in the feature space. In general many tracking algorithms use these features. The details of visual features are:
1. Color: The apparent color of an object is influenced primarily by two physical factors, 1) the spectral power distribution of the illuminant and 2) the surface reflectance properties of the objects. In image processing, the RGB (red, green, blue) color space is usually used to represent color.
2. Edges: Object boundaries usually generate strong changes in image intensities. Edge detection is used to identify these changes. Algorithms that track the boundary of the objects usually use edge as the representative feature.
3. Optical Flow: Is a dense field of displacement vectors which defines the translation of each pixel in a region. It is computed using the brightness constraints, which assumes brightness constancy of corresponding pixels in the consecutive frames.
4. Texture: Texture is the measure of the intensity variation of the surface whitch quantifies properties such as smoothness and regularity.
ALGORITHM FOR OBJECT TRACKING
Background subtraction in video using Bayesian learning:
An accurate and fast background subtraction technique for object tracking in still camera videos. Regions of motion in a frame are first estimated by comparing the current frame to a previous one. A sampling re-sampling based Bayesian learning technique is then used on the estimated regions to perform background subtraction and accurately determine the exact pixels which correspond to moving objects. An obvious advantage in
terms of processing time is gained as the Bayesian learning steps are performed only on the estimated motion regions, which typically constitute only a small fraction of the frame. The technique has been used on a variety of indoor and outdoor sequences, to track both slow and fast moving objects, under different lighting conditions and varying object-background contrast.
This algorithm presents robust system that achieves both (1) high speed and (2) high degrees of sensitivity compared to existing techniques. To achieve these objectives a 2
step tracking system has been used.
1) Motion Region Estimation
2) Bayesian Sampling Resampling
Motion Region Estimation: The Block Matching Algorithm (BMA) is a standard way of encoding video frames. A simplified variation of the BMA algorithm is used for determining regions of each frame which have had motion relative to a reference frame. Such regions have been called regions of motion. Each incoming frame is divided into non-overlapping blocks of equal size. Each block is compared to the corresponding block in the reference frame and the Sum of Absolute Difference (SAD) is determined for the block. The reference frame may be chosen to be a few frames before the current frame, to account for slow moving objects.