caa a22 4500 605446946 CHVBK 20210128100129.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s11042-014-2078-z doi (NATIONALLICENCE)springer-10.1007/s11042-014-2078-z Adaptive Kalman Filter Incorporated Eigenhand (AKFIE) for real-time hand tracking system [Elektronische Daten] [Mohd Mohd Asaari, Bakhtiar Rosdi, Shahrel Suandi] Hand tracking is one of the essential elements in vision-based hand gesture recognition systems. It has a great potential as a tool for better human-computer interaction (HCI) by means of communicating in natural and articulate ways. This has motivated an active research concerning with the interpretation of hand tracking for gesture recognition system. However, due to the nature of hand motion which is flexible, erratic and always varies in its appearance, the tracking of human hand using vision-based remains a complex problem. In this paper, we present an efficient method to overcome such difficulties using integration of Adaptive Kalman Filter (AKF) and Eigenhand method. In the proposed method, the tracking task is first carried out by running the Region of Interest (ROI) based tracker. Here, by fusing the skin and motion cues as the main tracking features, the actual hand position in the current frame is measured. This initial measurement is rather inconsistent due to the working principle of ROI based tracker which greatly depends on how effectively the extracted tracking features are. To reduce this inconsistency, the measurement error is minimized by employing the AKF prediction. After the hand position is effectively estimated by the AKF tracker, a low dimensional eigenspace representation, i.e., the Eigenhand, is employed to further improve the tracking performance. This representation is necessary as the AKF tracker only treats the hand image as a set of moving pixel which consequently disregards the detail appearance of the target. Therefore, the incorporated eigenspace representation provides a compact description of the internal target appearance for better object recognition. This eigenspace adaptively learns the current state to reflect the appearance changes of the target image in each frame. The experimental results demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large pose changes, lighting variation, fast motion and partial occlusion with average detection rate above 97% at the speed of 35 frame/second (fps). Springer Science+Business Media New York, 2014 Hand tracking nationallicence Adaptive Kalman Filter nationallicence Eigenspace nationallicence Eigenhand nationallicence Mohd Asaari Mohd Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), 14300, Nibong Tebal, Pulau Pinang, Malaysia aut Rosdi Bakhtiar Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), 14300, Nibong Tebal, Pulau Pinang, Malaysia aut Suandi Shahrel Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), 14300, Nibong Tebal, Pulau Pinang, Malaysia aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/21(2015-11-01), 9231-9257 1380-7501 74:21<9231 2015 74 11042 https://doi.org/10.1007/s11042-014-2078-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-2078-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Mohd Asaari Mohd Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), 14300, Nibong Tebal, Pulau Pinang, Malaysia aut NATIONALLICENCE 700 1- Rosdi Bakhtiar Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), 14300, Nibong Tebal, Pulau Pinang, Malaysia aut NATIONALLICENCE 700 1- Suandi Shahrel Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), 14300, Nibong Tebal, Pulau Pinang, Malaysia aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/21(2015-11-01), 9231-9257 1380-7501 74:21<9231 2015 74 11042