Object detection brings up several challenges in pattern recognition and computer vision, such as identifying and detecting various objects, and finding the location of each object in overlapping images. In object detection, the "object" is identified by the image given as input and "location" of that object is traced. Currently, there exists several algorithms that analyze the input image and provide output in terms of the detected objects, where each of them is associated with the class label as well as location (having all the coordinates of the bounding box) [1]. Among all of the object detection approaches, SSD (Single Shot Detector) is considered to be robust and fast since it uses multiple convolution for the detection of object. This blog will discuss about the SSD, its significant parameters and working of SSD.

We are using NVIDIA Jetson Nano Developer kit as our accelerator system.Which will contain Docker Container which will contain the dataset and trained model SSD (Single Shot Detector) MobileNetV2 which we will be used to for facial detection. Video would be recorded through the Camera attached to the accelerator system. Code of the SSD (Single Shot Detector) MobileNetV2 is written in Python Programming Language and Deep learning framework which has been used is PyTorch.To optimized the neural network layers.NVIDIA TensorRT is used for faster Inference during the run time.

Today's blog post is broken down into three parts. In the first part we'll discuss the origin of the more accurate OpenCV face detectors and where they live inside the OpenCV library. From there I'll demonstrate how you can perform face detection in images using OpenCV and deep learning. I'll then wrap up the blog post discussing how you can apply face detection to video streams using OpenCV and deep learning as well. Back in August 2017, OpenCV 3.3 was officially released, bringing it with it a highly improved "deep neural networks" ( dnn) module.

A couple weeks ago we learned how to classify images using deep learning and OpenCV 3.3's deep neural network ( dnn) module. While this original blog post demonstrated how we can categorize an image into one of ImageNet's 1,000 separate class labels it could not tell us where an object resides in image. In order to obtain the bounding box (x, y)-coordinates for an object in a image we need to instead apply object detection. Object detection can not only tell us what is in an image but also where the object is as well. In the remainder of today's blog post we'll discuss how to apply object detection using deep learning and OpenCV.