Title
Technical Papers Parallel Session-I: Comparison of UAV based person detection and tracking techniques
Abstract/Description
Motion detection, based on UAV surveillance, is one of the challenging task due to the continuous motion of the camera. In this paper, image alignment based on feature detection and feature matching has been presented using FAST algorithm which provides the important features whereas both RANSAC and MSAC algorithm is used to estimate the affine transformation utilizing a filter algorithm to remove the particular noise and make system easier. To detect the motion of a person, optical flow and background subtraction techniques are applied. Moreover, both techniques are compared for each ego-motion compensation algorithm used and subsequent results have been shown.
Keywords
Motion detection, Image segmentation, Computer vision, Optical flow, Background segmentation, SIFT algorithm
Location
Theatre 1, Aman Tower
Session Theme
Technical Papers Parallel Session-I: Speech, Image, and Vision Systems
Session Type
Parallel Technical Session
Session Chair
Dr. Tahir Qasim
Start Date
30-12-2017 2:00 PM
End Date
30-12-2017 2:20 PM
Recommended Citation
Yousuf, B. M., Qazi, H. A., & Hussain, Z. (2017). Technical Papers Parallel Session-I: Comparison of UAV based person detection and tracking techniques. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2017/2017/7
COinS
Technical Papers Parallel Session-I: Comparison of UAV based person detection and tracking techniques
Theatre 1, Aman Tower
Motion detection, based on UAV surveillance, is one of the challenging task due to the continuous motion of the camera. In this paper, image alignment based on feature detection and feature matching has been presented using FAST algorithm which provides the important features whereas both RANSAC and MSAC algorithm is used to estimate the affine transformation utilizing a filter algorithm to remove the particular noise and make system easier. To detect the motion of a person, optical flow and background subtraction techniques are applied. Moreover, both techniques are compared for each ego-motion compensation algorithm used and subsequent results have been shown.
