Object Detection With The Help Of Deep Neural Networks
The Inspiration behind the Deep neural networks is the working of the human brain. It's working way is beyond "if-else conditions" ,the software predicts and gives solutions. With Deep neural network AI, there is no need for programming and coding to get the output for it. The conclusions are made based on learning and experiences (just like the human brain).
This learning is becoming an integral part of the digital world, across multiple various sectors. Some examples are Alexa, Siri, Google Assistant, product recommendations in Amazon, autonomous driving in Tesla, Prisma, and Face App. These are globally well-known virtual assistants. Deep Neural Network AI is transforming the whole world.
Object Detection is one of the important computer vision task used to detect instances of visual objects of certain classes (for example, humans, animals, cars, or buildings) in digital images such as photos or video frames. The goal of the object detection is to develop computational models that provide the most fundamental information needed by computer vision applications: “What objects are where?”.
Person detection is a variant of object detection that is used to detect a primary class “person” in images or video frames. Detecting people in video streams is also an important task in modern video surveillance systems. The recent deep learning algorithms provide robust person detection results to us.
Object detection is one of the most important fundamental problems of computer vision. It forms the basis of many other downstream computer vision tasks, for example given as, instance segmentation, image captioning, object tracking, and more. Specific object detection applications which can be seen today include pedestrian detection, animal detection, vehicle detection, people counting, face detection, text detection, pose detection, or number-plate recognition
Object Detection and Deep Neural Networks
In the last few years, the rapid advances of techniques have greatly accelerated the momentum of object detection. With the deep learning networks and the computing power of GPU’s, the performance of object detectors and trackers has greatly improved accordingly, achieving significant breakthroughs in object detection technology.
Image analysis is one of the most prominent fields which we all look after in deep learning. As images are easy to generate and handle, and they are exactly the right type of data for machine learning.Easy to understand for us, but difficult for computers. Not surprisingly, but image analysis plays a key role in the history of deep neural networks.
Given through an example- Imagine a self-driving car just like tesla that needs to detect other cars on the road. There are lots of complicated algorithms working up for object detection. They are required huge datasets, very deep convolutional networks and as well as long training times.
Machine learning is a branch of artificial intelligence (AI), and it essentially involves learning patterns from daily examples or sample data as the machine accesses the given data and has the ability to learn from it. Deep Learning is a specialized form of machine learning which involves learning in different stages.
The most recent example for this topic is the currently AI based detection of players and viewing their stats on the mobile for the Fifa World Cup 2022 on the FIFA+ application while in stadium .It's an extraordinary use of the technology for audience to make their experience more exhihilarating and also left me awestruck.
As seen in a video shared by a fan attending the match at the stadium, the user can point their phone at the pitch where the game is going. An overlay pops up, allowing them to view the stats of each player, their live speed, and passes completed, and all one needs to do is tap the player you want to keep track of. Further, the app also shows users the scenes inside the FIFA war room when approached for Video Assistant Referee (VAR) heat map from different camera angles.