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How does a motion tracking background subtraction algorithm works? by rajkiran rajkiran

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How does a motion tracking background subtraction algorithm works? by
Article Posted: 02/24/2013
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How does a motion tracking background subtraction algorithm works?

Science & Technology,Software
Motion tracking is the process in which the movement of object’s position is detected which relates with its surrounding atmosphere. This process can easily be availed with both switch and trigger as these are mechanical methods. For instance of piano playing by a user, the motion tracking systems detect the pressing of key and making of sound accordingly with the proper keys written on music note. Other better motion detection method is properly based on the tracking of electronic sensors and devices like infrared, camera and laser equipments in order to avail important data.

Methods of motion tracking When the matter comes to the most essential method of motion detection, it is none other than frame comparison algorithm in which two different frames are evaluated to one another with an aim to sense the changing position of the object. The algorithm identifies the object and takes the image of same and makes the comparison of it with the newer object which is captured with the stream of video. This looks over many aspects with its simplicity like bad lighting conditions, shadows and false detection. While there are so many techniques of motion tracking, there are three most important and basic techniques of motion detection that are also used in many other algorithms.

1. Temporal Differencing: this technique uses the main rule to compare successive frames with an aim to capture the object’s movement. This is used widely in the dynamic environments. But this has limited accuracy to identify or extract the real movement and shape of the object which is the only drawback of that. However, integration with application is easy because this algorithm is simple for that.

2. Background Subtraction: In simple words, this is a technology to use a background’s model and match this up with recent picture. In this way, this displays the objects of foreground in the environment. The only condition is that the video capturing device i.e. the camera should be stationary with the background. Otherwise, the method is suitable and flexible of the statistical model which is depending upon the subtraction of volume. Though this technique is quicker and flexible, this is not so accurate for high-end environments.

3. Optical Flow: This uses the movement of pixels of two successive images. This specifies the number of pixels to move with taking two concoctive frames into the project. The best part is that this is highly efficient in any dynamic environment. In order to achieve the results of giant accuracy, the changing of environment and movement can’t affect this. In this way the higher the image and video source quality, the higher will be the accurate results. This technique compares movement of pixels of objects. This is so highly effective that camera positions and background changes can’t affect the accuracy. The only condition is that it requires high-speed professional camera devices.

Methods of gesture recognition At the time of making the identification of gesture of a predefined object, the human motion can be identified with the process into a machine-oriented language. In this way, the computer users make use of captured gestures with their available input devices and the software systems. All these captures gestures by the input methods are based totally on the key press by the user.

The type of input of data and the utilization of gesture recognized are based on the methods of tracking. As far as gestures tracking and object’s movement is concerned, the available algorithm should be designed and capable to compare the original gestures and other movements in order to provide the needed results as per the identified gesture. In addition, the gestures should be defined in a proper way. If it is not so, there are chances when these gestures are more likely to be identified as a different meaning and it may tends to get an unintentional results. This can be defined more clearly with an example of sign language in which each gesture defines its own meaning which is quite easier to consider and it is universal.

Basically, there are three different methods of creating the algorithms of gesture recognition:

1. 3D model based: This uses volumetric models in order to differentiate gestures and motion. It makes a 3D structure of a predefined object and records the gestures and movement of the same by using motion and data gloves and other advanced sensors. This creates the models from the polygon meshes and other sophisticated three-dimensional surfaces in order to model accurate human gestures. Though it demands for huge computing power for high accuracy which is its only drawback with high hardware needs, there are some new methods have been investigated with an aim to reduce its requirement.

2. Skeletal based models: As the name suggests, this algorithm is simply designed on the basis of skeletal appearance of human object. This uses skeletal parameters of joints and segments with their orientation to make a dynamic skeletal structure of human body which is especially for gesture recognition. This is quite faster algorithm as it includes parameters of smallest numbers and easily helps to focus on specific parts on the body.

3. Appearance oriented models: Since this takes images and videos directly, the matter of using a definite body representation doesn’t arise in its part. This algorithm basically uses 2D templates that are deformable with its outline points. This method is far much easier and simpler than 3D and skeletal oriented models. However, this is quite less accurate than both above algorithms.

In conclusion These above are the basic and widely used technologies and algorithms for detecting motions and gestures respectively. In order to cater lots of requirements of software platforms, there are lots of algorithms and methods in the today’s world. For the lighting conditions and the hardware and software quality, the algorithms of recognition are limited. The limitation is also relies on the technology which is used for data tracking that can also affect the gesture recognition accuracy at a great extent. In this way, accuracy with proper hardware resources is a must when it comes to get the robust detection of motion with robust algorithm.

Get the use of good algorithms of motion tracking.with good resources available in

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