Project Outline/Description
Object recognition in urban settings is a technology that can be used to detect and identify objects in a cityscape. This technology can be used to identify objects such as cars, buildings, people, and other landmarks. It can also be used to identify objects in video streams, such as pedestrians, cyclists, and vehicles. Object recognition can be used to detect and track objects in real-time, as well as to analyze their behavior. This technology can be used in a variety of applications, such as surveillance, traffic monitoring, and navigation.
Dataset overview
The research team at the University of Technology in, India, generated the publicly accessible dataset for use in the object recognition challenge. The offered dataset’s main goal is to assist computer vision engineers with performing and analysing object recognition or localization tasks. The collection consists of some raw photos divided into three primary categories, including vehicles, people, and bicycles. Additionally, the collection includes 380 photos that do not belong to any of the above classes. The chosen dataset has high complexity because of intra-class variations including occlusion, object scales, viewing angle,some image examples.
Applications
Object recognition is applied in many areas of computer vision, including image retrieval, security, surveillance, automated vehicle parking systems and ma- chine inspection. Significant challenges stay on the field of object recognition.
Requirements
Hardware requirements for object recognition in urban environments typically include a camera, computer, and a processor.
The camera should be capable of capturing high-resolution images and videos.
The processor should be powerful enough to handle the demands of the object recognition algorithm.
The computer should have enough storage space to store the images and videos.
- Processor – Dual Core.
- Speed – 1.1 G Hz.
- RAM – 8 GB (min).
- Hard Disk – 20 GB.
Software requirements for object recognition in urban environments typically include image processing libraries, a machine learning library, and an object recognition library. Image processing libraries are used to process and analyze images. Machine learning libraries are used to create and train the object recognition algorithms. Object recognition libraries are used to identify and classify objects in the images and videos.
- Operating System –Windows 10.Linux,Ubuntu
- Technology – Machine Learning.
- IDLE- Python 3.7 or higher.
What you will learn?
Gained experience in using computer vision algorithms to recognize objects in urban environments.
Learn how to apply object detection and image recognition techniques to identify items in urban scenes, such as buildings, roads, cars, and pedestrians.
Additionally, you will have gained an understanding of how to use deep learning methods to improve your object recognition results.
Curriculum
- 7 Sections
- 7 Lessons
- 15 Hours
- Introduction to Computer Vision1
- Object Detection Techniques1
- Image Recognition Algorithms1
- Deep Learning for Object Recognition1
- Implementing Object Recognition in Urban Environments1
- Evaluation and Performance Analysis1
- Conclusion1
