TY - GEN
T1 - A Real-Time Video Analysis With an Omni-Directional Camera for Multi Object Detection Using The Hough Transform Method
AU - Hikmahwan, Bagus
AU - Hario, Fakhriy
AU - Mudjirahardjo, Panca
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Many computer vision applications require multi-object detection, such as robotics, surveillance, and autonomous vehicles. Hough Transform (HT) is a popular method for object detection in digital images, but its application to real-time video streams is limited. In this study, we propose a method for multi-object detection in the form of ball and goalpost using the HT algorithm on real-time video captured by an Omni-Directional camera. The proposed method consists of two main steps: the first step involves applying the HT algorithm to the video stream to detect potential object locations. The second step involves implementing a clustering algorithm to group candidate locations into object instances. Our method includes a pre-processing step to filter out the color of specific HSV objects and remove noise. Experimental results on real-time video can detect objects in the form of balls with an accuracy rate of 72.19% and goalpost with an accuracy rate of 80% in some random video streams with an Omni-Directional camera. So that in the future Omni-Directional cameras can become a valuable tool for various computer vision applications.
AB - Many computer vision applications require multi-object detection, such as robotics, surveillance, and autonomous vehicles. Hough Transform (HT) is a popular method for object detection in digital images, but its application to real-time video streams is limited. In this study, we propose a method for multi-object detection in the form of ball and goalpost using the HT algorithm on real-time video captured by an Omni-Directional camera. The proposed method consists of two main steps: the first step involves applying the HT algorithm to the video stream to detect potential object locations. The second step involves implementing a clustering algorithm to group candidate locations into object instances. Our method includes a pre-processing step to filter out the color of specific HSV objects and remove noise. Experimental results on real-time video can detect objects in the form of balls with an accuracy rate of 72.19% and goalpost with an accuracy rate of 80% in some random video streams with an Omni-Directional camera. So that in the future Omni-Directional cameras can become a valuable tool for various computer vision applications.
KW - Computer Vision
KW - Hough Transform
KW - HSV Color Filtering
KW - Multi Object Detection
KW - Omni-Directional Camera
UR - https://www.scopus.com/pages/publications/85190068484
U2 - 10.1109/ICE-SMARTECH59237.2023.10461966
DO - 10.1109/ICE-SMARTECH59237.2023.10461966
M3 - Conference contribution
AN - SCOPUS:85190068484
T3 - 2023 1st IEEE International Conference on Smart Technology: Advances in Smart Technology for Sustainable Well-Being, ICE-SMARTec 2023
SP - 118
EP - 123
BT - 2023 1st IEEE International Conference on Smart Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023
Y2 - 17 July 2023 through 19 July 2023
ER -