Upright FAST-Harris Filter

Abdulmalik Danlami Mohammed*, Saliu Adam Muhammed**, Idris Mohammed Kolo***, Adama Victor Ndako****, Shafi’i Muhammed Abdulhamid*****, Abdulkadir Baba Hassan******, Abubakar Saddiq Mohammed*******
*_****Lecturer, Department of Computer Science, Federal University of Technology, Minna, Nigeria.
*****Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria
*** ***Associate Professor, Department of Mechanical Engineering, Federal University of Technology, Minna, Nigeria
*******Department of Electrical/Electronic Engineering, Federal University of Technology, Minna, Nigeria
Periodicity:July - September'2018

Abstract

The traditional approaches to the classification of image regions suffer drawbacks in the face of imaging conditions (occlusion, illumination changes, rotation, viewpoint changes, and image blurring) and thus contribute to the poor performance of several vision based applications, such as object recognition, object tracking, image retrieval, pose estimation, camera calibration, 3D reconstruction, Structure from motion, stereo images, and image stitching. However, in this work, feature points extraction method by decomposition of image structure is employed in order to overcome these challenges. The decomposition of an image structure into feature set enhances the performance of many vision-based applications and system. The feature point extraction method which we refer to as Upright Feature from Accelerated Segment Test with Harris filter (UFAH) in this text, works by combining Feature from Accelerated Segment Test detector with Harris filter. The result obtained in the evaluation process shows that UFAH is robust and also invariant to imaging conditions (i.e rotation, illumination changes, and image blurring).

Keywords

Image Analysis, Feature Points, Repeatability, Harris Filter, FAST, Upright FAST

How to Cite this Article?

Mohammed, A.D., Saliu, A.M., Kolo, I.M., Ndako, A.V., Abdulhamid, S.M., Hassan, A.B., and Mohammed, A.S (2018). Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos. i-manager’s Journal on Image Processing, 5(3),14-20.

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