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The object detection is used to detect the object by using deep neural using this process we can get more accuracy. before than that we used k means algorithm. but we can get only less accuracy. so, we can move to neural gives the better accuracy. for the identification we are taking blob detect ion. basically, used at the commercial applications, face identification and object tracking, image retrieval, and automated parking system. this project we will dive deeper and look at various algorithms that can be used for object detection. we will start with the algorithms belonging to rcnn family, i.e. rcnn, fast rcnn and faster rcnn. in the upcoming article of this series, we will cover more advanced algorithms.

The object detection is applied to the objects of the human being. not only human being we can detect any type of images. this process will be applied to detect the object for the applications of wide range of industries, image retrieval etc.. algorithm applies a neural network to an entire image. the network divides the image into an s x s grid and comes up with bounding boxes, which are boxes drawn around images and predicted probabilities for each of these regions. the method used to come up with these probabilities is logistic regression. the bounding boxes are weighted by the associated probabilities. for class prediction, independent logistic classifiers are used. in this article, i am going to demonstrate how to implement the NN algorithm with a pre trained model. first, we would need to install darknet. it is a neural network framework that is open source. and by using blob detection we can identify the image by the rectangle box. using this process we can detect objects easily.

Existing Method:-

edge detection:

edge detection?is an image processing technique for finding the boundaries of objects within images. it works by detecting discontinuities in brightness. edge detection is used for?image segmentation?and data extraction in areas such as image processing, computer vision, and machine vision.

morphological filter:

the idea of the morphological filter are shrink and let grow process. the word ?shrink? means using median filter to round off the large structures and to remove the small structures and in grow process, remaining structures are grow back by the same amount.the morphological operation of the binary image is described first and will talk in the following outline.outlines are the structuring element of a binary filter, dilation and erosion, composite operation.

the structuring element:

in morphological filter, each element in the matrix is called ?structuring element? instead of coefficient matrix in the linear filter. the structuring elements contain only value 0 and 1. and the hot spot of the filter is the dark shade element.

Proposed Methodology:

video streaming:

video streaming technology is one way to deliver video over the internet. ?using streaming technologies, the delivery of audio and video over the internet can reach many millions of customer using their personal computers, pdas, mobile smartphones or other streaming devices. the reasons for?video streaming technology growth are:

  • broadband networks are being deployed
  • video and audio compression techniques are more efficient
  • quality and variety of audio and video services over internet are increasing

there are two major ways for the transmission of video/audio information over the internet:

download mode.?the content file is completely downloaded and then played. this mode requires long downloading time for the whole content file and requires hard disk space.

streaming mode.?the content file is not required to be downloaded completely and it is playing while parts of the content are being received and decoded.

In this project we detect the object detection based on the blob detection and darknet.? this can be used in real-time applications which require object detection for pre-processing in their pipeline. an important scope would be to train the system on a video sequence for usage in tracking applications. addition of a temporally consistent network would enable smooth detection and more optimal than per-frame detection.

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