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RISS 인기검색어
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- 저자 : S. M. Omair (검색결과 2 건)
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A Low Cost Electro-Oculogram (EOG) Controlled Assistive Wheel Chair
Raheel Riaz,S.Hammad Akhter,Aisha Masood,Ana Zulfiqar,Khoula Abid,Kiran Akhter,S. M. Omair,Zia Mohyud-din 한국재활복지공학회 2016 한국재활복지공학회 학술대회논문집 Vol.2016 No.11
Increased Paralysis or physical disability cases are one of the greatest problems of this technologically developing world. It destroys the life and self-esteem of the survivor and they become dependable on others to fulfill their mobility needs. Many works has been done to aid their disability and to give them independent mobility. Several sophisticated methods have been designed to generate such assistive devices to help the disable peoples. Some of such devices include the use of cameras to produce movement based on eye gaze, another such method consists of the use of high-end computer systems that can process the data efficiently but these are very expensive and less portable. This research introduce a cost effective method to generate assistive device to guide and control the wheel chair for disable people, it involves the non-sophisticated technology based on analog filters, amplifiers and Electrooculography (measure of the Corneo-retinal standing potential that exists between the front and the back of the human eye.). Eye movement directions are used to guide and control the wheel chair.
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Online multi-object tracking via robust collaborative model and sample selection
Naiel, Mohamed A.,Ahmad, M. Omair,Swamy, M.N.S.,Lim, Jongwoo,Yang, Ming-Hsuan Elsevier 2017 Computer vision and image understanding Vol.154 No.-
<P><B>Abstract</B></P> <P>The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a collaborative model between a pre-trained object detector and a number of single-object online trackers within the particle filtering framework. For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker. We present a motion model that incorporates the associated detections with object dynamics. Furthermore, we propose an effective sample selection scheme to update the appearance model of each tracker. We use discriminative and generative appearance models for the likelihood function and data association, respectively. Experimental results show that the proposed scheme generally outperforms state-of-the-art methods.</P>
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