Object_Detection_Live_Capturing

Object detection is need of time. For easy living there is need for technological advancements. It facilitates the needs and provide a robust solutions. I have used Deep Neural Network algorithm over COCO model to detect and recognize object. It detects multiple objects in real-time videos. It also detects multiple objects at a time in a given image sequence. We have tested the proposal system over of 30 different classes with 300 different images. The accuracy is 94.30 %. All the extensions required for this object detection project, such as OPENCV and NUMPY have been imported from Visual Studio Code through tools provided to me by Microsoft. This is how Object Detection project compiled successfully. But I wanted to store all these files safely on Microsoft Azure platform. So I took advantage of the services of Microsoft azure that freely provided to me. I created a Microsoft storage account to save project files safely. The share capacity of the storage account was 5 TB, of which 100 GB I have been used for storing the project files. Created a storage account on the Microsoft azure for the object detection project called “objectdeteazurestorage”. To save files in it, I created a file share account called "objdetfiles”. Once all of these services were created, all of the project's files were uploaded to Azure Storage Account “objedeteazurestorage” via Visual Code.