Video Object Detection using Particle Filters
Sharda Mahajan · 2012
Wepropose an object detection method using particle filters. Our approach estimates the probability of object presence in the current image given the history of observations up to current time. To do so, object presence is modeled by a two-state Markov chain,and the problem is translated into sequential Bayesian estimation which can be solved by particle filters. The observation density, required by the particle filter is based on selected discriminative Aar-like features that were introduced by Viola and Jones [6] for object detection in static images. We illustrate the approach on the problem of face detection. Experiments on real video sequences show the feasibility of the approach. This paper will explain why should we prefer the particle filters than any other method require to detect and recognize the object and also it gives the basic information about the kalman filters[5] the disadvantages of it and how they are removed in the particle filters[3].it will also give the basic steps of particle filters. Generally recognition of the objects in video can offers significant benefits to video retrieval including automatic annotation and content based queries based on the object characteristic. Detecting particular object in video is an important step toward semantic understanding of visual imagery.