International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Object Detection with Computer Vision Rakesh Kumar Mahey1, Tanima Arora2 1MCA Student, Department of Computer Application, D.A.V Institute of Engineering and Technology,
Jalandhar,144008, Punjab, India 2MCA Student, Department of Computer Application, D.A.V Institute of Engineering and Technology,
Jalandhar,144008, Punjab, India --------------------------------------------------------------------------***-------------------------------------------------------------------------
Abstract The goal of this research was to use [1]human hands to run computers, mainly to support and improve technology used in the field of education, especially for supporting lecturers during presentations. The programme created for this goal makes use of additional libraries including [2]FLTK, [3]OpenGL, and [4]OpenCV, as well as [5]computer vision techniques. Presenters need a projector and a webcam in order to use the programme. The programme sends appropriate signals to the computer based on the[6] recognised patterns by using the webcam to detect and analyse the shape and pattern of the presenter's hands. The stud y's output is a programme that successfully improves [7]teaching and presentation techniques, giving teachers a more seamless experience. In the subject of [8]computer vision, there is now a lot of work being done on the identification and localization of visually appealing regions inside images. Applications in [8]computer vision, [9]computer graphics, and multimedia can all benefit from the ability to automatically [10]recognise and partition such salient regions. Many salient object detection [11](SOD) techniques have been developed to imitate the capacity of the human visual system to identify salient areas in images. Based on how they engineer features, these techniques can be generally divided into two groups: deep learning-based methods and conventional methods. This survey covers both traditional and deep learning-based methods, including the most significant developments in image-based SOD. The survey offers in-depth insights on saliency modelling trends, addressing important concerns, outlining fundamental methods, and exploring potential future directions.
Keywords: human hand detection FLTK, OpenGL, OpenCV, computer vision techniques, recognized patterns, teaching presentation techniques, computer vision, computer graphics, recognize, (SOD) techniques
1.Introduction The key task of salient object detection (SOD), which is based on the properties of the human visual system (HVS), is to precisely recognize and separate visually different sections inside images. The pre-attentive phase of the HVS, which focuses human attention on the most intriguing parts of a scene, is emulated by SOD models. In order to maximize efficiency and make the most use of available resources, following high-level vision tasks may benefit from the identification of salient regions in images.
© 2023, IRJET
|
Impact Factor value: 8.226
|
ISO 9001:2008 Certified Journal
|
Page 1839