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Visual tracking in image streams acquired by static cameras based on change detection by raspberry pi
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Visual tracking in image streams acquired by static cameras based on change detection by raspberry pi

Category : Raspberry PI

Sub Category : Image processing

Project Code : ITRAP04

Project Abstract


Visual tracking in image streams acquired by static cameras is usually based on change detection and recursive Bayesian estimation, such an approach laying at the core of many practical applications. Yet, the interaction between the change detector and the Bayesian filter is typically designed heuristically. Differently, this paper develops a sound framework to model and implement a bidirectional communication flow between the two processes. In our Bayesian loop, change detection provides welldefined observation likelihood to the recursive filter and the filter prediction provides an informative prior to the change detector, which deploys Bayesian reasoning alike. The loop is developed for the two major variants of Bayesian filters used in tracking, namely the Kalman filter and the particle filter. Experiments on publicly available videos and a novel challenging data set show that the proposed interaction scheme outperforms several state-of-the-art trackers..

Existing System

Proposed system


Unidirectional communication flow between the processes.

Drawbacks of existing system:

    Static technique

    Difficult in real time applications

    More computations are required

     Less efficiency

     Less flexible


Bidirectional communication flow between the processes so that efficiency is improved.

Advantages of proposed system:

  Dynamic technique

  Easy to operate

  Less computations are required

  High efficiency

     It is flexible to use and portable

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