Comparison between Horn-Schunck and Brox techniques for Motion Detection in Thermal Video

Ramli, Suzaimah; Othman, Norzila; Zainudin, Norulzahrah Mohd; Wahab, N. S. A.; Hasbullah, Nor Asiakin; Zizi, T. Tan; Azizi, F.; Ibrahim, Norazlin

doi:10.1109/icictm.2016.7890771

Cited by 2 publications

(1 citation statement)

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“…The optical flow method is more accurate in object detection, but the calculation of the optical flow field is complex, and this method is prone to noise interference. The most common optical flow methods used for object detection are the H-S method ( Ramli et al, 2016 ) and the L-K method ( Patel & Upadhyay, 2013 ). The inter frame difference method is used to differentiate two or three adjacent and consecutive frames of images to obtain the contour of a moving object ( Chi, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Global vision object detection using an improved Gaussian Mixture model based on contour

Sun

2024

PeerJ Computer Science

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Object detection plays an important role in the field of computer vision. The purpose of object detection is to identify the objects of interest in the image and determine their categories and positions. Object detection has many important applications in various fields. This article addresses the problems of unclear foreground contour in moving object detection and excessive noise points in the global vision, proposing an improved Gaussian mixture model for feature fusion. First, the RGB image was converted into the HSV space, and a mixed Gaussian background model was established. Next, the object area was obtained through background subtraction, residual interference in the foreground was removed using the median filtering method, and morphological processing was performed. Then, an improved Canny algorithm using an automatic threshold from the Otsu method was used to extract the overall object contour. Finally, feature fusion of edge contours and the foreground area was performed to obtain the final object contour. The experimental results show that this method improves the accuracy of the object contour and reduces noise in the object.

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Section: Introductionmentioning

confidence: 99%