Raspberry pi based single object tracking using Bayesian filter example
Bhor DipaliAmbadas, Shekhar H. Bodake · 2017
In recent trends the movable object detection is locating its position & location with reference of higher weighted particles. The color based target detection & tracking is the main role for developing the application like video streaming, research area like color template matching processing & open source visual surveillance area. A Bayesian filtering method & video analysis modeling is required for locating the position of object & template matching under the segmentation area of the interest for the movable objects which comprises evolutionary modules. The extended kalman filter method is used different areas just like video streaming, monitoring application, counting & extraction. The position & tracking the location of single movable object is implemented on the basis of extended kalman filter. The design of video streaming system is directed the evolutionary application for formalization of specific parameter just like especially tracking & location of given moving target. The recent development process is demonstrated by the Bayesian filtering method. This method is including an advance technique & very desirable methodology for signal processing with highly usable the region of application. The particle filter is depends on performing step by step sampling with generation of discrete sets of pdf's of set of particles. By using of color based algorithm the particle filter method is solving the drawbacks of kalman filter. It is included combination of higher & lower level segmentation function & algorithm such as object detection, features matching & tracking. The ARM based raspberry Pi Model 2 is obtaining on line video tracking by using Open source Linux OS.