Description

Object Detection using Bag of Words

                       In this project, object detection  plays a critical  role  in  robust  image  recognition  systems,  and  can be applied in a multitude of applications, ranging from simple monitoring to advanced tracking. In this paper it is analyzed the usage of the Bag of Words model to efficiently detect and recognize objects that can appear in different scales, orientations and even from different  perspective views.  This  approach relies  in image analysis  techniques,  such  as  feature  detection,  description  and clustering, in order to be able to recognize the target object even if it is present in cluttered environments. For supporting the recognition in different perspective views, machine learning techniques are used  to build  a model  of the  target objects.  This model can  then be employed to successfully  detect if an  instance of the target object is present in an image. For pinpointing the location of the target object, a sliding window method is used in conjunction with dynamic thresholding.This experimental results gives the high accuracy and efficiency.

               

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