Machine learning is a subset of artificial intelligence where statistical methods are used to help a computer improve at a task with training and experience. Object Detection and Tracking in Machine Learning are widely used in Computer Vision. We can also deploy this Technology on the cloud with the help of various cloud vendors like Microsoft Azure. This topic is covered in [AI-900] Microsoft Certified Azure AI Fundamentals Course.
In this post, we will cover
- Overview of Object Detection and Tracking
- Object Detection on Azure
- Algorithms
- Real-Life Applications
Overview Of Object Detection And Tracking
Object Detection and Tracking in Machine Learning are among the widely used technology in various fields of IT industries. Object identification is a type of AI-based PC vision in which a model is prepared to perceive singular kinds of items in a picture and to distinguish their area in the picture. An item can be a face, a human, a line of individuals, as well as a product on an assembly line.
Also Read : Our Previous Blog on AutoML ( Automated Machine Learning )
Object Detection:
Object detection is a technology related to computer vision and image processing that deals with detecting and locating objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
Check Out: Our Previous Blog On Azure Cognitive Services.
Object Tracking:
Object tracking is a field that can help to track moving objects when they move across several video frames with the help of Machine-learning. In Machine-learning accuracy and analysis power of object detection vastly improved. Objects can be people, but may also be animals, vehicles, or other objects of interest such as the ball in a game of soccer.
Also read: Azure Core Identity Services – Azure AD & MFA
Object Detection On Azure
The Custom Vision cognitive service in Azure is used to create object detection models on the azure cloud. This meets the needs of many computer vision scenarios and doesn’t require expertise in deep learning and a lot of training images.
We can use the following types of resources to create an Object detection model
- Custom Vision: A dedicated resource for the custom vision service, which can be either a Training or a Prediction resource.
- Cognitive Services: A general cognitive services resource that includes Custom Vision along with many other cognitive services. We can use this type of resource for Training, Prediction, or both.
Also Check: features of Azure Machine Learning Studio
Creating an Object Detection model using Custom Vision consists of three main tasks.
1) Upload & Tag Images: First we need to upload some images and tag them with the labels (like a car, bus, human) which we use as training data for model creation
2) Training Model: Train the object detection model with this image data so that the model will learn the pattern in the images to make further inferences.
Note: The efficiency & accuracy of the model created is directly proportional to the training of the model.
3) Publish the Model: Use the trained model on some test data (images) and check the accuracy of the model created.
Check out: Overview of Azure Machine Learning Service
Algorithms
There are some useful Algorithms like HOG, SORT, GOTURN, and MDNet for Object detection and tracking in Machine-learning, Nowhere is a HOG algorithm described.
Histogram of oriented gradients (HOG): HOG is a feature descriptor. A feature descriptor is a representation of an image or parts of an image known as patches that extract useful information for the model to interpret, such as information like human or textual data and ignores the background. The HOG descriptor technique counts occurrences of gradient orientation in localized portions of an image-detection window, or region of interest.
Read more: MLOps is based on DevOps principles and practices that increase the efficiency of workflows and improve the quality and consistency of the machine learning solutions.
Real-Life Application
- People Counting: Object detection can be utilized for People counting. It is utilized for group measurements during festivals or in the Mall as well.
- Automated CCTV surveillance: Using this technology CCTV camera can be upgraded and it can automatically detect the objects and send useful information to the admin.
- Self Driving Cars: Using Object Detection Technology, No need to drive a car it can be run automatically.
- Face detection and Face recognition: Is widely used in security and on various social media platforms (like face unlock systems, Facebook)
- Identity verification through IRIS code: Iris recognition is one of the most accurate identity verification systems which uses an Object Detection & Tracking algorithm.
- Ball tracking in Sports: Record the video frame according to the movement of the ball automatically
Also, Read Our Blog Post On DP 100 Exam.
Related/References:
- [DP-100] Microsoft Certified Azure Data Scientist Associate: Everything you must know
- [AI-900] Microsoft Certified Azure AI Fundamentals Course: Everything you must know
- Microsoft Certified Azure Data Scientist Associate | DP 100 | Step By Step Activity Guides (Hands-On Labs)
- Certified Kubernetes Administrator (CKA) Certification Exam: Everything You Must Know
- For Exam DP-100: Designing and Implementing a Data Science Solution on Azure
- [DP-100] Designing and Implementing a Data Science Solution on Azure
- Microsoft Azure Data Scientist DP-100 FAQ
- Overview of Hyperparameter Tuning In Azure
Next Task For You
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Udaykumar Shroff says
Very nice brother…!!
Rahul Dangayach says
Hi Udaykumar,
Glad you liked our blog.
Stay tuned for more informative blogs.
Thanks and Regards
Rahul Dangayach
Team K21 Academy
Kunal Narle says
Very well explained.
Rahul Dangayach says
Hi Kunal,
Glad you liked our blog.
Stay tuned for more informative blogs.
Thanks and Regards
Rahul Dangayach
Team K21 Academy